
Professor Ricardo Gabrielli Barreto Campello
Professor
School of Mathematical and Physical Sciences
- Email:ricardo.campello@newcastle.edu.au
- Phone:(02) 4921 6762
Career Summary
Biography
Research: His current research interests fall primarily within the areas of data mining and machine learning, especially the design of new algorithms and mathematical tools for descriptive and predictive analytics, such as clustering, outlier detection, pattern classification, regression, and other related tasks. Particular emphasis is given to unsupervised and semi-supervised statistical learning techniques. Past research projects also involved topics in computational intelligence (neural networks, fuzzy systems, evolutionary computation) and modelling of dynamic systems.
Although his work focuses primarily on conceptual, general-purpose methods that can be used in a wide spectrum of application domains, Ricardo has also a track record of interdisciplinary collaborations towards more specialised, application-oriented research. For instance, he is or was involved in projects and student supervision in areas such as bioinformatics (gene-expression data analysis), webmedia (recommender systems), time-series mining of oceanographic and energy demand data, and control/automation of biochemical processes. He is keen to discuss opportunities for future partnerships, particularly in the realm of data science, big data analytics, and their applications to various fields.
Ricardo has published 115+ research papers in scholarly journals, book chapters, and peer-reviewed conference proceedings, with about 6000 citations detected by the Google Scholar database and a h-index = 34 as of February/2021 (Scopus h-index = 27).
Brief Biography: Prof. Ricardo Campello received his Bachelor degree in Electronics Engineering from the State University of São Paulo, Brazil, in 1994, and his MSc and PhD degrees in Electrical and Computer Engineering from the State University of Campinas, Brazil, in 1997 and 2002, respectively. Among other previous appointments, he was a Post-doctoral Fellow at the University of Nice, France (fall/winter 2002 - 2003), an Assistant/Associate Professor in computer science at the University of São Paulo, Brazil (2007 - 2016), and a Visiting Professor in computer science at the University of Alberta, Canada (2011 - 2013), where he is currently an Adjunct Professor (since July/2017). From Nov/2016 to June/2018 he was a Full Professor in applied mathematics/statistics at the College of Science and Engineering, James Cook University, Townsville, QLD, Australia, where he is currently an Adjunct Professor (since July/2018). In July 2018 he joined the discipline of statistics within the School of Mathematical and Physical Sciences of the University of Newcastle, as Professor of Data Science.
Ricardo is a merit scholar of the Brazilian National Research Council (CNPq) since 2005, a distinction held only by a small fraction of Brazilian academics. He is an accredited primary PhD supervisor at the University of São Paulo - Brazil, a top-ranked university in Latin America, as well as an accredited PhD co-supervisor at the University of Alberta, Canada. He is also an accredited primary PhD supervisor (advisor mentor) at James Cook University (JCU), where he played a central role in the design and development of a new professional Master of Data Science online program. Ricardo has also played a pivotal role in the design and development of the Online Master of Data Science in the University of Newcastle, as well as in the creation/establishment of the double degree program Master of Business Administration (MBA) / Master of Science (Data Analytics), and he has served as Convenor for these programs since 2019. He has served as an Associate Editor for the international journal Computational Intelligence by Wiley since 2015. He has also regularly served as a member/senior member of the program committee for major international conferences on Data Science and Big Data Analytics, and as a member of the guest editorial board for special issues of renowned journals such as Machine Learning (Springer) and Data Mining and Knowledge Discovery (Springer).
Ricardo Campello's short résumé is available here.
Prospective Students and Post-docs:
Candidates seeking research positions at honours, MSc, PhD and Post-doctoral levels are welcome to contact me to discuss possible projects and funding/scholarship opportunities from various schemes in Australia and abroad.
People in my group have the opportunity to work with cutting-edge research in data mining, machine learning, data science, big data, and applications, in collaboration with renowned international experts.
Students with a passion for problem solving using a combination of statistics (e.g. multivariate analysis), applied mathematics (e.g. optimisation and discrete maths), and/or computer science (e.g. algorithm analysis and design, programming challenges, data structures) are particularly encouraged to apply.
Qualifications
- PhD (Electrical Engineering), Universidade Estadual de Campinas - Brazil
- Bachelor of Science (Electrical Engineering), Sao Paulo State University, Brazil
- Master in Electrical Engineering, Universidade Estadual de Campinas - Brazil
Keywords
- Big Data
- Data Analytics
- Data Mining
- Data Science
- Machine Learning
- Predictive Analytics
- Statistical Learning
Languages
- Portuguese (Mother)
- English (Fluent)
Professional Experience
UON Appointment
Title | Organisation / Department |
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Professor | University of Newcastle School of Mathematical and Physical Sciences Australia |
Professor | University of Newcastle School of Mathematical and Physical Sciences Australia |
Academic appointment
Dates | Title | Organisation / Department |
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1/11/2016 - 30/6/2018 | Professor | James Cook University College of Science and Engineering Australia |
1/10/2002 - 31/1/2003 | Post-Doctoral Research Fellow | University of Nice Sophia Antipolis Laboratoire D´Informatique, Signaux et Systèmes (I3S) France |
1/7/2011 - 30/10/2016 | Associate Professor | University of São Paulo Institute of Mathematics and Computer Science Brazil |
1/8/2011 - 31/7/2013 | Visiting Professor | University of Alberta Department of Computing Science Canada |
1/1/2007 - 30/6/2011 | Assistant Professor | University of São Paulo Institute of Mathematics and Computer Science Brazil |
1/4/2003 - 31/12/2006 | Research Associate and Guest Lecturer | State University of Campinas School of Electrical and Computer Engineering Brazil |
1/7/2003 - 20/12/2006 | Assistant Professor | Catholic University of Santos Postgraduate Program in Informatics Brazil |
1/7/2017 - 30/6/2020 | Adjunct Professor | University of Alberta Department of Computing Science Canada |
1/7/2018 - 30/6/2021 | Adjunct Professor | James Cook University College of Science and Engineering Australia |
1/3/2005 - 28/2/2022 | Merit Scholar | Brazilian National Research Council (CNPq) Brazil |
Membership
Dates | Title | Organisation / Department |
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1/1/2015 - | Associate Editor | Computational Intelligence Journal (Wiley) United States |
Awards
Award
Year | Award |
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2017 |
ACM Computing Reviews: 21st (2016) Annual Best of Computing - Notable Article Association for Computing Machinery (ACM) |
2013 |
Merit Scholar of the Brazilian National Research Council (CNPq) - Level 1 Brazilian National Research Council (CNPq) |
2005 |
Merit Scholar of the Brazilian National Research Council (CNPq) - Level 2 Brazilian National Research Council (CNPq) |
Distinction
Year | Award |
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2020 |
Top 2% Scientist in the World (Updated Science-Wide Author Databases of Standardized Citation Indicators) Stanford University |
Publications
For publications that are currently unpublished or in-press, details are shown in italics.
Chapter (6 outputs)
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2017 |
Rabbany R, Takaffoli M, Fagnan J, Zaïane O, Gabrielli Barreto Campello RJ, 'Relative Validity Criteria for Community Mining Algorithms', Encyclopedia of Social Network Analysis and Mining, Springer, New York, NY (2017)
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2015 |
Vendramin L, Campello RJGB, Naldi MC, 'Fuzzy clustering algorithms and validity indices for distributed data', Partitional Clustering Algorithms 147-192 (2015) © Springer International Publishing Switzerland 2015. This chapter presents a unified framework to generalize a number of fuzzy clustering algorithms to handle distributed data in... [more] © Springer International Publishing Switzerland 2015. This chapter presents a unified framework to generalize a number of fuzzy clustering algorithms to handle distributed data in an exact way, i.e., with no approximation of results with respect to their original centralized versions. The same framework allows the exact distribution of relative validity indices used to evaluate the quality of fuzzy clustering solutions. Complexity analyses for each distributed algorithm and index are reported in terms of space, time, and communication aspects. A general procedure to estimate the number of clusters in a non¿centralized fashion using the proposed framework is also described. Such a procedure is directly applicable not only to distributed data, but to parallel data processing scenarios as well. Experimental results illustrate the speedup obtained when running algorithms under the proposed framework in multiple cores of a processor, when compared to their traditional, centralized counterparts running in a single core. Additionally, the quality of the results and amount of data transmitted are assessed and compared among different fuzzy clustering algorithms.
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2009 |
Horta D, Naldi M, Campello RJGB, Hruschka ER, Carvalho ACPLF, 'Evolutionary fuzzy Ccustering: An overview and efficiency issues', 167-195 (2009) Clustering algorithms have been successfully applied to several data analysis problems in a wide range of domains, such as image processing, bioinformatics, crude oil analysis, ma... [more] Clustering algorithms have been successfully applied to several data analysis problems in a wide range of domains, such as image processing, bioinformatics, crude oil analysis, market segmentation, document categorization, and web mining. The need for organizing data into categories of similar objects has made the task of clustering very important to these domains. In this context, there has been an increasingly interest in the study of evolutionary algorithms for clustering, especially those algorithms capable of finding blurred clusters that are not clearly separated from each other. In particular, a number of evolutionary algorithms for fuzzy clustering have been addressed in the literature. This chapter has two main contributions. First, it presents an overview of evolutionary algorithms designed for fuzzy clustering. Second, it describes a fuzzy version of an evolutionary algorithm for clustering, which has shown to be more computationally efficient than systematic (i.e., repetitive) approaches when the number of clusters in a data set is unknown. Illustrative experiments showing the influence of local optimization on the efficiency of the evolutionary search are also presented. These experiments reveal interesting aspects of the effect of an important parameter found in many evolutionary algorithms for clustering, namely, the number of iterations of a given local search procedure to be performed at each generation. © 2009 Springer-Verlag Berlin Heidelberg.
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2007 |
Hruschka ER, de Castro LN, Campello RJGB, 'Clustering gene-expression data: A hybrid approach that iterates between k-means and evolutionary search', 313-335 (2007) Clustering genes based on their expression profiles is usually the first step in gene-expression data analysis. Among the many algorithms that can be applied to gene clustering, t... [more] Clustering genes based on their expression profiles is usually the first step in gene-expression data analysis. Among the many algorithms that can be applied to gene clustering, the k-means algorithm is one of the most popular techniques. This is mainly due to its ease of comprehension, implementation, and interpretation of the results. However, k-means suffers from some problems, such as the need to define a priori the number of clusters (k) and the possibility of getting trapped into local optimal solutions. Evolutionary algorithms for clustering, by contrast, are known for being capable of performing broad searches over the space of possible solutions and can be used to automatically estimate the number of clusters. This work elaborates on an evolutionary algorithm specially designed to solve clustering problems and shows how it can be used to optimize the k-means algorithm. The performance of the resultant hybrid approach is illustrated by means of experiments in several bioinformatics datasets with multiple measurements, which are expected to yield more accurate and more stable clusters. Two different measures (Euclidean and Pearson) are employed for computing (dis)similarities between genes. A review of the use of evolutionary algorithms for gene-expression data processing is also included. © 2007 Springer-Verlag Berlin Heidelberg.
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2001 |
Meleiro LAC, Maciel R, Campello RJGB, Amaral WC, 'Hierarchical neural fuzzy models as a tool for process identification: A bioprocess application', , IMPERIAL COLLEGE PRESS 173-196 (2001)
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Show 3 more chapters |
Journal article (55 outputs)
Year | Citation | Altmetrics | Link | |||||
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2020 |
de Aguiar Neto FS, da Costa AF, Manzato MG, Campello RJGB, 'Pre-processing approaches for collaborative filtering based on hierarchical clustering', Information Sciences, 534 172-191 (2020) [C1]
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2020 |
Marques HO, Campello RJGB, Sander J, Zimek A, 'Internal Evaluation of Unsupervised Outlier Detection', ACM Transactions on Knowledge Discovery from Data, 14 (2020) [C1]
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2020 |
Campello RJGB, Kröger P, Sander J, Zimek A, 'Density-based clustering', Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10 (2020) [C1]
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2019 |
da Costa AF, Manzato MG, Campello RJGB, 'Boosting collaborative filtering with an ensemble of co-trained recommenders', Expert Systems with Applications, 115 427-441 (2019) [C1]
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2019 |
Cavalcante Araujo Neto A, Sander J, Campello R, Nascimento M, 'Efficient Computation and Visualization of Multiple Density-Based Clustering Hierarchies', IEEE Transactions on Knowledge and Data Engineering, (2019) IEEE HDBSCAN*, a state-of-the-art density-based hierarchical clustering method, produces a hierarchical organization of clusters in a dataset w.r.t. a parameter mpts. While small ... [more] IEEE HDBSCAN*, a state-of-the-art density-based hierarchical clustering method, produces a hierarchical organization of clusters in a dataset w.r.t. a parameter mpts. While small changes in mpts typically lead to a small change in the clustering structure, choosing a “good” mpts value can be challenging: depending on the data distribution, a high or low mpts value may be more appropriate, and certain clusters may reveal themselves at different values. To explore results for a range of mpts values, one has to run HDBSCAN* for each value independently, which can be computationally impractical. In this paper, we propose an approach to efficiently compute all HDBSCAN* hierarchies for a range of mpts values by building upon results from computational geometry to replace HDBSCAN*'s complete graph with a smaller equivalent graph. An experimental evaluation shows that our approach can obtain over one hundred hierarchies for the computational cost equivalent to running HDBSCAN* about twice, which corresponds to a speedup of more than 60 times, compared to running HDBSCAN* independently that many times. We also propose a series of visualizations that allow users to analyze a collection of hierarchies for a range of mpts values, along with case studies that illustrate how these analyses are performed.
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2019 |
Soares VHA, Campello RJGB, Nourashrafeddin S, Milios E, Naldi MC, 'Combining semantic and term frequency similarities for text clustering', Knowledge and Information Systems, 61 1485-1516 (2019) [C1]
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2019 |
Castro Gertrudes J, Zimek A, Sander J, Campello RJGB, 'A unified view of density-based methods for semi-supervised clustering and classification', Data Mining and Knowledge Discovery, 33 1894-1952 (2019) [C1]
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2017 |
Oliveira GV, Coutinho FP, Campello RJGB, Naldi MC, 'Improving k-means through distributed scalable metaheuristics', NEUROCOMPUTING, 246 45-57 (2017)
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2017 |
Padilha VA, Campello RJGB, 'A systematic comparative evaluation of biclustering techniques', BMC BIOINFORMATICS, 18 (2017)
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2016 |
Jaskowiak PA, Moulavi D, Furtado ACS, Campello RJGB, Zimek A, Sander J, 'On strategies for building effective ensembles of relative clustering validity criteria', KNOWLEDGE AND INFORMATION SYSTEMS, 47 329-354 (2016)
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2016 |
Campos GO, Zimek A, Sander J, Campello RJGB, Micenkova B, Schubert E, et al., 'On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study', DATA MINING AND KNOWLEDGE DISCOVERY, 30 891-927 (2016)
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2015 |
Machado JB, Campello RJGB, Amaral WC, 'Asymmetric Volterra Models Based on Ladder-Structured Generalized Orthonormal Basis Functions', IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 60 2879-2891 (2015)
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2015 |
Naldi MC, Campello RJGB, 'Comparison of distributed evolutionary k-means clustering algorithms', NEUROCOMPUTING, 163 78-93 (2015)
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2015 |
Horta D, Campello RJGB, 'Comparing Hard and Overlapping Clusterings', JOURNAL OF MACHINE LEARNING RESEARCH, 16 2949-2997 (2015)
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2015 |
Campello RJGB, Moulavi D, Zimek A, Sander J, 'Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection', ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA, 10 (2015)
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2014 |
Naldi MC, Campello RJGB, 'Evolutionary k-means for distributed data sets', NEUROCOMPUTING, 127 30-42 (2014)
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2014 |
Jaskowiak PA, Campello RJGB, Costa IG, 'On the selection of appropriate distances for gene expression data clustering', BMC BIOINFORMATICS, 15 (2014)
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2014 |
Horta D, Campello RJGB, 'Similarity Measures for Comparing Biclusterings', IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, 11 942-954 (2014)
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2013 |
Naldi MC, Carvalho ACPLF, Campello RJGB, 'Cluster ensemble selection based on relative validity indexes', DATA MINING AND KNOWLEDGE DISCOVERY, 27 259-289 (2013)
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2013 |
Campello RJGB, Moulavi D, Zimek A, Sander J, 'A framework for semi-supervised and unsupervised optimal extraction of clusters from hierarchies', DATA MINING AND KNOWLEDGE DISCOVERY, 27 344-371 (2013)
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2013 |
Jaskowiak PA, Campello RJGB, Costa IG, 'Proximity Measures for Clustering Gene Expression Microarray Data: A Validation Methodology and a Comparative Analysis', IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, 10 845-857 (2013)
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2013 |
Rabbany R, Takaffoli M, Fagnan J, Zaïane OR, Campello RJGB, 'Communities validity: methodical evaluation of community mining algorithms', Social Network Analysis and Mining, 3 1039-1062 (2013) © 2013, Springer-Verlag Wien. Grouping data points is one of the fundamental tasks in data mining, which is commonly known as clustering if data points are described by attributes... [more] © 2013, Springer-Verlag Wien. Grouping data points is one of the fundamental tasks in data mining, which is commonly known as clustering if data points are described by attributes. When dealing with interrelated data, that is represented in the form a graph wherein a link between two nodes indicates a relationship between them, there has been a considerable number of approaches proposed in recent years for mining communities in a given network. However, little work has been done on how to evaluate the community mining algorithms. The common practice is to evaluate the algorithms based on their performance on standard benchmarks for which we know the ground-truth. This technique is similar to external evaluation of attribute-based clustering methods. The other two well-studied clustering evaluation approaches are less explored in the community mining context; internal evaluation to statistically validate the clustering result and relative evaluation to compare alternative clustering results. These two approaches enable us to validate communities discovered in a real-world application, where the true community structure is hidden in the data. In this article, we investigate different clustering quality criteria applied for relative and internal evaluation of clustering data points with attributes and also different clustering agreement measures used for external evaluation and incorporate proper adaptations to make them applicable in the context of interrelated data. We further compare the performance of the proposed adapted criteria in evaluating community mining results in different settings through extensive set of experiments.
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2013 |
Machado JB, Campello RJGB, Amaral WC, 'Takagi-Sugeno Fuzzy Models in the Framework of Orthonormal Basis Functions', IEEE TRANSACTIONS ON CYBERNETICS, 43 858-870 (2013)
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2012 |
Horta D, Campello RJGB, 'Automatic aspect discrimination in data clustering', PATTERN RECOGNITION, 45 4370-4388 (2012)
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2012 |
Coletta LFS, Vendramin L, Hruschka ER, Campello RJGB, Pedrycz W, 'Collaborative Fuzzy Clustering Algorithms: Some Refinements and Design Guidelines', IEEE TRANSACTIONS ON FUZZY SYSTEMS, 20 444-462 (2012)
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2012 |
Campello RJGB, Moulavi D, Sander J, 'A Simpler and More Accurate AUTO-HDS Framework for Clustering and Visualization of Biological Data', IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, 9 1850-1852 (2012)
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2012 |
Oliveira GHC, Da Rosa A, Campello RJGB, MacHado JB, Amaral WC, 'An introduction to models based on Laguerre, Kautz and other related orthonormal functions - Part II: Non-linear models', International Journal of Modelling, Identification and Control, 16 1-14 (2012) This paper provides an overview of system identification using orthonormal basis function models, such as those based on Laguerre, Kautz, and generalised orthonormal basis functio... [more] This paper provides an overview of system identification using orthonormal basis function models, such as those based on Laguerre, Kautz, and generalised orthonormal basis functions. The paper is separated in two parts. The first part of the paper approached issues related with linear models and models with uncertain parameters. Now, the mathematical foundations as well as their advantages and limitations are discussed within the contexts of non-linear system identification. The discussions comprise a broad bibliographical survey of the subject and a comparative analysis involving some specific model realisations, namely, Volterra, fuzzy, and neural models within the orthonormal basis functions framework. Theoretical and practical issues regarding the identification of these non-linear models are presented and illustrated by means of two case studies. Copyright © 2012 Inderscience Enterprises Ltd.
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2011 |
Horta D, de Andrade IC, Campello RJGB, 'Evolutionary fuzzy clustering of relational data', THEORETICAL COMPUTER SCIENCE, 412 5854-5870 (2011)
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2011 |
Naldi MC, Campello RJGB, Hruschka ER, Carvalho ACPLF, 'Efficiency issues of evolutionary k-means', APPLIED SOFT COMPUTING, 11 1938-1952 (2011)
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2011 |
Oliveira GHC, Da Rosa A, Campello RJGB, Machado JB, Amaral WC, 'An introduction to models based on Laguerre, Kautz and other related orthonormal functions - Part I: Linear and uncertain models', International Journal of Modelling, Identification and Control, 14 121-132 (2011) This paper provides an overview of system identification using orthonormal basis function models, such as those based on Laguerre, Kautz, and generalised orthonormal basis functio... [more] This paper provides an overview of system identification using orthonormal basis function models, such as those based on Laguerre, Kautz, and generalised orthonormal basis functions. The paper is separated in two parts. In this first part, the mathematical foundations of these models as well as their advantages and limitations are discussed within the context of linear and robust system identification. The second part approaches the issues related with non-linear models. The discussions comprise a broad bibliographical survey of the subjects involving linear models within the orthonormal basis functions framework. Theoretical and practical issues regarding the identification of these models are presented and illustrated by means of a case study involving a polymerisation process. Copyright © 2011 Inderscience Enterprises Ltd.
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2010 |
Campello RJGB, 'Generalized external indexes for comparing data partitions with overlapping categories', PATTERN RECOGNITION LETTERS, 31 966-975 (2010)
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2010 |
Vendramin L, Campello RJGB, Hruschka ER, 'Relative clustering validity criteria: A comparative overview', Statistical Analysis and Data Mining, 3 209-235 (2010) Many different relative clustering validity criteria exist that are very useful in practice as quantitative measures for evaluating the quality of data partitions, and new criteri... [more] Many different relative clustering validity criteria exist that are very useful in practice as quantitative measures for evaluating the quality of data partitions, and new criteria have still been proposed from time to time. These criteria are endowed with particular features that may make each of them able to outperform others in specific classes of problems. In addition, they may have completely different computational requirements. Then, it is a hard task for the user to choose a specific criterion when he or she faces such a variety of possibilities. For this reason, a relevant issue within the field of clustering analysis consists of comparing the performances of existing validity criteria and, eventually, that of a new criterion to be proposed. In spite of this, the comparison paradigm traditionally adopted in the literature is subject to some conceptual limitations. The present paper describes an alternative, possibly complementary methodology for comparing clustering validity criteria and uses it to make an extensive comparison of the performances of 40 criteria over a collection of 962,928 partitions derived from five well-known clustering algorithms and 1080 different data sets of a given class of interest. A detailed review of the relative criteria under investigation is also provided that includes an original comparative asymptotic analysis of their computational complexities. This work is intended to be a complement of the classic study reported in 1985 by Milligan and Cooper as well as a thorough extension of a preliminary paper by the authors themselves. © 2010 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 3: 209-235, 2010.
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2009 |
da Rosa A, Campello RJGB, Amaral WC, 'Exact Search Directions for Optimization of Linear and Nonlinear Models Based on Generalized Orthonormal Functions', IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 54 2757-2772 (2009)
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2009 |
Campello RJGB, Hruschka ER, 'On comparing two sequences of numbers and its applications to clustering analysis', INFORMATION SCIENCES, 179 1025-1039 (2009)
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2009 |
Hruschka ER, Campello RJGB, Freitas AA, de Carvalho ACPLF, 'A Survey of Evolutionary Algorithms for Clustering', IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND REVIEWS, 39 133-155 (2009)
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2009 |
Campello RJGB, Hruschka ER, Alves VS, 'On the efficiency of evolutionary fuzzy clustering', JOURNAL OF HEURISTICS, 15 43-75 (2009)
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2008 |
Da Rosa A, Campello RJGB, Amaral WC, 'An optimal expansion of Volterra models using independent Kautz bases for each kernel dimension', INTERNATIONAL JOURNAL OF CONTROL, 81 962-975 (2008)
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2007 |
da Rosa A, Campello RJGB, Amaral WC, 'Choice of free parameters in expansions of discrete-time Volterra models using Kautz functions', AUTOMATICA, 43 1084-1091 (2007)
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2007 |
Campello RJGB, Oliveira GHC, Amaral WC, 'Identification and control of processes via developments in the orthonormal series Part A: Identification _net Identificação e controle de processos via desenvolvimentos em séries ortonormais. Parte A: Identificação', Controle y Automacao, 18 301-321 (2007) In this paper, an overview about the identification of dynamic systems using orthonormal basis function models, such as those based on Laguerre and Kautz functions, is presented. ... [more] In this paper, an overview about the identification of dynamic systems using orthonormal basis function models, such as those based on Laguerre and Kautz functions, is presented. The mathematical foundations of these models as well as their advantages and limitations are discussed within the contexts of linear, robust, and nonlinear identification. The discussions comprise a broad bibliographical survey on the subject and a comparative analysis involving some specific model realizations, namely, linear, Volterra, fuzzy, and neural models within the orthonormal basis function framework. Theoretical and practical issues regarding the identification of these models are also presented and illustrated by means of two case studies related to a polymerization process.
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2007 |
Oliveira GHC, Campello RJGB, Amaral WC, 'Identification and control of processes via developments in the orthonormal series Part B: Predictive control', Controle y Automacao, 18 322-336 (2007) This paper presents an overview about predictive control schemes based on orthonormal basis function models. Different predictive control schemes based on such models are discusse... [more] This paper presents an overview about predictive control schemes based on orthonormal basis function models. Different predictive control schemes based on such models are discussed, namely, linear controllers with terminal (stabilizing) constraints, robust controllers, and non-linear controllers. The discussions comprise a broad bibliographical survey on the subject as well as two case studies involving a simulated dynamic system and a real process.
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2007 |
Campello RJGB, 'A fuzzy extension of the Rand index and other related indexes for clustering and classification assessment', PATTERN RECOGNITION LETTERS, 28 833-841 (2007)
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2006 |
Campello RJGB, do Amaral WC, Favier G, 'A note on the optimal expansion of Volterra models using Laguerre functions', AUTOMATICA, 42 689-693 (2006)
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2006 |
Hruschka ER, Campello RJGB, de Castro LN, 'Evolving clusters in gene-expression data', INFORMATION SCIENCES, 176 1898-1927 (2006)
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2006 |
Campello RJGB, Hruschka ER, 'A fuzzy extension of the silhouette width criterion for cluster analysis', FUZZY SETS AND SYSTEMS, 157 2858-2875 (2006)
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2006 |
Meleiro LAC, Campello RJGB, Maciel Filho R, Amaral WC, 'Application of hierarchical neural fuzzy models to modeling and control of a bioprocess', APPLIED ARTIFICIAL INTELLIGENCE, 20 797-816 (2006)
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2006 |
Campello RJGB, Caradori do Amaral W, 'Hierarchical fuzzy relational models: Linguistic interpretation and universal approximation', IEEE TRANSACTIONS ON FUZZY SYSTEMS, 14 446-453 (2006)
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2004 |
Campello RJGB, Favier G, do Amaral WC, 'Optimal expansions of discrete-time Volterra models using Laguerre functions', AUTOMATICA, 40 815-822 (2004)
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2003 |
Campello RJGB, Von Zuben FJ, Amaral WC, Meleiro LAC, Maciel R, 'Hierarchical fuzzy models within the framework of orthonormal basis functions and their application to bioprocess control', CHEMICAL ENGINEERING SCIENCE, 58 4259-4270 (2003)
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2003 |
Campello RJGB, Amaral WC, 'Towards true linguistic modelling through optimal numerical solutions', INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE, 34 139-157 (2003)
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2001 |
Campello RJGB, Amaral WC, 'Modeling and linguistic knowledge extraction from systems using fuzzy relational models', FUZZY SETS AND SYSTEMS, 121 113-126 (2001)
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1998 |
Campello RJGB, Nazzetta RM, do Amaral WC, 'A highly adaptive algorithm for fuzzy modelling of systems', INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS, 6 35-50 (1998)
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Show 52 more journal articles |
Conference (54 outputs)
Year | Citation | Altmetrics | Link | |||||
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2019 |
dos Anjos FDAR, Gertrudes JC, Sander J, Campello RJGB, 'A modularity-based measure for cluster selection from clustering hierarchies', Data Mining. 16th Australasian Conference, AusDM 2018, Bathurst, NSW (2019) [E1]
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2018 |
da Costa AF, Manzato MG, Campello RJGB, 'CoRec: A Co-Training Approach for Recommender Systems', 33RD ANNUAL ACM SYMPOSIUM ON APPLIED COMPUTING, Univ Pau Pays Adour, Pau, FRANCE (2018)
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2018 |
da Costa A, Fressato E, Neto F, Manzato M, Campello R, 'Case Recommender: A Flexible and Extensible Python Framework for Recommender Systems', 12TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS), Vancouver, CANADA (2018)
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2017 |
Neto ACA, Sander J, Campello RJGB, Nascimento MA, 'Efficient Computation of Multiple Density-Based Clustering Hierarchies', 2017 17TH IEEE INTERNATIONAL CONFERENCE ON DATA MINING (ICDM), New Orleans, LA (2017)
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2016 |
Da Costa AF, Martins RD, Manzato MG, Campello RJGB, 'Exploiting different users' interactions for profiles enrichment in recommender systems', Proceedings of the ACM Symposium on Applied Computing (2016) © 2016 ACM. User profiling is an important aspect of recommender systems. It models users' preferences and is used to assess an item's relevance to a particular user. In... [more] © 2016 ACM. User profiling is an important aspect of recommender systems. It models users' preferences and is used to assess an item's relevance to a particular user. In this paper we propose a profiling approach which describes and enriches the users' preferences using multiple types of interactions. We show in our experiments that the enriched version of users' profiles is able to provide better recommendations.
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2016 |
Da Costa AF, Manzato MG, Campello RJGB, 'Group-based collaborative filtering supported by multiple users' feedback to improve personalized ranking', WebMedia 2016 - Proceedings of the 22nd Brazilian Symposium on Multimedia and the Web (2016) © 2016 ACM. Recommender systems were created to represent user preferences for the purpose of suggesting items to purchase or examine. However, there are several optimizations to ... [more] © 2016 ACM. Recommender systems were created to represent user preferences for the purpose of suggesting items to purchase or examine. However, there are several optimizations to be made in these systems mainly with respect to modeling the user profile and remove the noise information. This paper proposes a collaborative filtering approach based on preferences of groups of users to improve the accuracy of recommendation, where the distance among users is computed using multiple types of users' feedback. The advantage of this approach is that relevant items will be suggested based only on the subjects of interest of each group of users. Using this technique, we use a state-of-art collaborative filtering algorithm to generate a personalized ranking of items according to the preferences of an individual within each cluster. The experimental results show that the proposed technique has a higher precision than the traditional models without clustering.
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2016 |
Swersky L, Marques HO, Sander J, Campello RJGB, Zimek A, 'On the Evaluation of Outlier Detection and One-Class Classification Methods', PROCEEDINGS OF 3RD IEEE/ACM INTERNATIONAL CONFERENCE ON DATA SCIENCE AND ADVANCED ANALYTICS, (DSAA 2016), Montreal, CANADA (2016)
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2016 |
Batista AJL, Campello RJGB, Sander J, 'Active Semi-Supervised Classification based on Multiple Clustering Hierarchies', PROCEEDINGS OF 3RD IEEE/ACM INTERNATIONAL CONFERENCE ON DATA SCIENCE AND ADVANCED ANALYTICS, (DSAA 2016), Montreal, CANADA (2016)
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2015 |
Jaskowiak PA, Campello RJGB, 'A Cluster Based Hybrid Feature Selection Approach', 2015 BRAZILIAN CONFERENCE ON INTELLIGENT SYSTEMS (BRACIS 2015), Natal, BRAZIL (2015)
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2015 |
Marques HO, Campello RJGB, Zimek A, Sander J, 'On the Internal Evaluation of Unsupervised Outlier Detection', PROCEEDINGS OF THE 27TH INTERNATIONAL CONFERENCE ON SCIENTIFIC AND STATISTICAL DATABASE MANAGEMENT, Univ Calif San Diego, San Diego, CA (2015)
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2014 |
Li J, Sander J, Campello R, Zimek A, 'Active Learning Strategies for Semi-Supervised DBSCAN', ADVANCES IN ARTIFICIAL INTELLIGENCE, CANADIAN AI 2014, Montreal, CANADA (2014)
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2014 |
Zimek A, Campello RJGB, Sander J, 'Data perturbation for outlier detection ensembles', ACM International Conference Proceeding Series (2014) Outlier detection and ensemble learning are well established research directions in data mining yet the application of ensemble techniques to outlier detection has been rarely stu... [more] Outlier detection and ensemble learning are well established research directions in data mining yet the application of ensemble techniques to outlier detection has been rarely studied. Building an ensemble requires learning of diverse models and combining these diverse models in an appropriate way. We propose data perturbation as a new technique to induce diversity in individual outlier detectors as well as a rank accumulation method for the combination of the individual outlier rankings in order to construct an outlier detection ensemble. In an extensive evaluation, we study the impact, potential, and shortcomings of this new approach for outlier detection ensembles. We show that this ensemble can significantly improve over weak performing base methods. © Copyright 2014 ACM 978-1-4503-2722-0/14/06¿$15.00.
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2014 |
Moulavi D, Jaskowiak PA, Campello RJGB, Zimek A, Sander J, 'Density-based clustering validation', SIAM International Conference on Data Mining 2014, SDM 2014 (2014) One of the most challenging aspects of clustering is validation, which is the objective and quantitative assessment of clustering results. A number of different relative validity ... [more] One of the most challenging aspects of clustering is validation, which is the objective and quantitative assessment of clustering results. A number of different relative validity criteria have been proposed for the validation of globular, clusters. Not all data, however, are composed of globular clusters. Density-based clustering algorithms seek partitions with high density areas of points (clusters, not necessarily globular) separated by low density areas, possibly containing noise objects. In these cases relative validity indices proposed for globular cluster validation may fail. In this paper we propose a relative validation index for density-based, arbitrarily shaped clusters. The index assesses clustering quality based on the relative density connection between pairs of objects. Our index is formulated on the basis of a new kernel density function, which is used to compute the density of objects and to evaluate the within- and between-cluster density connectedness of clustering results. Experiments on synthetic and real world data show the effectiveness of our approach for the evaluation and selection of clustering algorithms and their respective appropriate parameters.
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2014 |
Pourrajabi M, Zimek A, Moulavi D, Sander J, Campello RJGB, Goebel R, 'Model selection for semi-supervised clustering', Advances in Database Technology - EDBT 2014: 17th International Conference on Extending Database Technology, Proceedings (2014) Although there is a large and growing literature that tackles the semi-supervised clustering problem (i.e., using some labeled objects or cluster-guiding constraints like \must-li... [more] Although there is a large and growing literature that tackles the semi-supervised clustering problem (i.e., using some labeled objects or cluster-guiding constraints like \must-link" or \cannot-link"), the evaluation of semi-supervised clustering approaches has rarely been discussed. The application of cross-validation techniques, for example, is far from straightforward in the semi-supervised setting, yet the problems associated with evaluation have yet to be addressed. Here we summarize these problems and provide a solution. Furthermore, in order to demonstrate practical applicability of semi-supervised clustering methods, we provide a method for model selection in semi-supervised clustering based on this sound evaluation procedure. Our method allows the user to select, based on the available information (labels or constraints), the most appropriate clustering model (e.g., number of clusters, density-parameters) for a given problem.
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2013 |
Vendramin L, Jaskowiak PA, Campello RJGB, 'On the combination of relative clustering validity criteria', ACM International Conference Proceeding Series (2013) Many different relative clustering validity criteria exist that are very useful as quantitative measures for assessing the quality of data partitions. These criteria are endowed w... [more] Many different relative clustering validity criteria exist that are very useful as quantitative measures for assessing the quality of data partitions. These criteria are endowed with particular features that may make each of them more suitable for specific classes of problems. Nevertheless, the performance of each criterion is usually unknown a priori by the user. Hence, choosing a specific criterion is not a trivial task. A possible approach to circumvent this drawback consists of combining different relative criteria in order to obtain more robust evaluations. However, this approach has so far been applied in an ad-hoc fashion only; its real potential is actually not well-understood. In this paper, we present an extensive study on the combination of relative criteria considering both synthetic and real datasets. The experiments involved 28 criteria and 4 different combination strategies applied to a varied collection of data partitions produced by 5 clustering algorithms. In total, 427,680 partitions of 972 synthetic datasets and 14,000 partitions of a collection of 400 image datasets were considered. Based on the results, we discuss the shortcomings and possible benefits of combining different relative criteria into a committee. Categories and Subject Descriptors H.2.8 [Database Applications]: Data Mining General Terms Measurement, Experimentation Keywords clustering validation, relative validity criteria, combinations of validity criteria. Copyright © 2013 ACM.
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2013 |
Zimek A, Gaudet M, Campello RJGB, Sander J, 'Subsampling for efficient and effective unsupervised outlier detection ensembles', Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2013) Copyright © 2013 ACM. Outlier detection and ensemble learning are well established research directions in data mining yet the application of ensemble techniques to outlier detecti... [more] Copyright © 2013 ACM. Outlier detection and ensemble learning are well established research directions in data mining yet the application of ensemble techniques to outlier detection has been rarely studied. Here, we propose and study subsampling as a technique to induce diversity among individual outlier detectors. We show analytically and experimentally that an outlier detector based on a subsample per se, besides inducing diversity, can, under certain conditions, already improve upon the results of the same outlier detector on the complete dataset. Building an ensemble on top of several subsamples is further improving the results. While in the literature so far the intuition that ensembles improve over single outlier detectors has just been transferred from the classification literature, here we also justify analytically why ensembles are also expected to work in the unsupervised area of outlier detection. As a side effect, running an ensemble of several outlier detectors on subsamples of the dataset is more efficient than ensembles based on other means of introducing diversity and, depending on the sample rate and the size of the ensemble, can be even more efficient than just the single outlier detector on the complete data.
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2013 |
Campello RJGB, Moulavi D, Sander J, 'Density-based clustering based on hierarchical density estimates', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2013) We propose a theoretically and practically improved density-based, hierarchical clustering method, providing a clustering hierarchy from which a simplified tree of significant clu... [more] We propose a theoretically and practically improved density-based, hierarchical clustering method, providing a clustering hierarchy from which a simplified tree of significant clusters can be constructed. For obtaining a "flat" partition consisting of only the most significant clusters (possibly corresponding to different density thresholds), we propose a novel cluster stability measure, formalize the problem of maximizing the overall stability of selected clusters, and formulate an algorithm that computes an optimal solution to this problem. We demonstrate that our approach outperforms the current, state-of-the-art, density-based clustering methods on a wide variety of real world data. © Springer-Verlag 2013.
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2013 |
Naldi MC, Gabrielli Barreto Campello RJ, 'Distributed k-means Clustering with Low Transmission Cost', 2013 BRAZILIAN CONFERENCE ON INTELLIGENT SYSTEMS (BRACIS), Fortaleza, BRAZIL (2013)
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2012 |
Rabbany R, Takaffoli M, Fagnan J, Zaiane OR, Campello RJGB, 'Relative Validity Criteria for Community Mining Algorithms', 2012 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING (ASONAM), Kadir Has Univ, Istanbul, TURKEY (2012)
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2012 |
Coelho Naldi M, Campello RJGB, 'Combining information from distributed evolutionary k-means', Proceedings - Brazilian Symposium on Neural Networks, SBRN (2012) One of the challenges for clustering resides in dealing with huge amounts of data, which causes the need for distribution of large data sets in separate repositories. However, mos... [more] One of the challenges for clustering resides in dealing with huge amounts of data, which causes the need for distribution of large data sets in separate repositories. However, most clustering techniques require the data to be centralized. One of them, the k-means, has been elected one of the most influential data mining algorithms. Although exact distributed versions of the k-means algorithm have been proposed, the algorithm is still sensitive to the selection of the initial cluster prototypes and requires that the number of clusters be specified in advance. This work tackles the problem of generating an approximated model for distributed clustering, based on k-means, for scenarios where the number of clusters of the distributed data is unknown. We propose a collection of algorithms that generate and select k-means clustering for each distributed subset of the data and combine them afterwards. The variants of the algorithm are compared from two perspectives: the theoretical one, through asymptotic complexity analyses, and the experimental one, through a comparative evaluation of results obtained from a collection of experiments and statistical tests. © 2012 IEEE.
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2012 |
Jaskowiak PA, Campello RJGB, Costa IG, 'Evaluating correlation coefficients for clustering gene expression profiles of cancer', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2012) Cluster analysis is usually the first step adopted to unveil information from gene expression data. One of its common applications is the clustering of cancer samples, associated ... [more] Cluster analysis is usually the first step adopted to unveil information from gene expression data. One of its common applications is the clustering of cancer samples, associated with the detection of previously unknown cancer subtypes. Although guidelines have been established concerning the choice of appropriate clustering algorithms, little attention has been given to the subject of proximity measures. Whereas the Pearson correlation coefficient appears as the de facto proximity measure in this scenario, no comprehensive study analyzing other correlation coefficients as alternatives to it has been conducted. Considering such facts, we evaluated five correlation coefficients (along with Euclidean distance) regarding the clustering of cancer samples. Our evaluation was conducted on 35 publicly available datasets covering both (i) intrinsic separation ability and (ii) clustering predictive ability of the correlation coefficients. Our results support that correlation coefficients rarely considered in the gene expression literature may provide competitive results to more generally employed ones. Finally, we show that a recently introduced measure arises as a promising alternative to the commonly employed Pearson, providing competitive and even superior results to it. © 2012 Springer-Verlag.
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2011 |
Vendramin L, Campello RJGB, Coletta LFS, Hruschka ER, 'Distributed Fuzzy Clustering with Automatic Detection of the Number of Clusters', INTERNATIONAL SYMPOSIUM ON DISTRIBUTED COMPUTING AND ARTIFICIAL INTELLIGENCE, Salamanca, SPAIN (2011)
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2011 |
Braga MF, MacHado JB, Campello RJGB, Do Amaral WC, 'Optimization of Volterra models with asymmetrical kernels based on generalized orthonormal functions', 2011 19th Mediterranean Conference on Control and Automation, MED 2011 (2011) An improved approach to determine exact search directions for the optimization of Volterra models based on Generalized Orthonormal Bases of Functions (GOBF) is proposed. The propo... [more] An improved approach to determine exact search directions for the optimization of Volterra models based on Generalized Orthonormal Bases of Functions (GOBF) is proposed. The proposed approach extends the work in [7], where a novel, exact technique for optimizing the GOBF parameters (poles) for Volterra models of any order was presented. The proposed extensions take place in two different ways: (i) the formulation here is derived in such a way that each multidimensional kernel of the model is decomposed into a set of independent orthonormal bases (rather than a single, common basis), each of which is parameterized by an individual set of poles intended for representing the dominant dynamic of the kernel along a particular dimension; and (ii) a novel, more computationally efficient method to analytically and recursively calculate the search directions (gradients) for the bases poles is derived. A simulated example is presented to illustrate the performance of the proposed approach. A comparison between the proposed method, which uses asymmetric kernels with multiple orthonormal bases, and the original method, which uses symmetric kernels with a single basis, is presented. © 2011 IEEE.
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2011 |
Horta D, Campello RJGB, 'Automatic aspect discrimination in relational data clustering', International Conference on Intelligent Systems Design and Applications, ISDA (2011) The features describing a data set may often be arranged in meaningful subsets, each of which corresponds to a different aspect of the data. An unsupervised algorithm (SCAD) that ... [more] The features describing a data set may often be arranged in meaningful subsets, each of which corresponds to a different aspect of the data. An unsupervised algorithm (SCAD) that performs fuzzy clustering and aspects weighting simultaneously was recently proposed. However, there are several situations where the data set is represented by proximity matrices only (relational data), which renders several clustering approaches, including SCAD, inappropriate. To handle this kind of data, the relational clustering algorithm CARD, based on the SCAD algorithm, has been recently developed. However, CARD may fail and halt given certain conditions. To fix this problem, its steps are modified and then reordered to also reduce the number of parameters required. The improved CARD is assessed over hundreds of real and artificial data sets. © 2011 IEEE.
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2010 | Coletta LFS, Hruschka ER, Covoes TF, Campello RJGB, 'Fuzzy Clustering-Based Filter', INFORMATION PROCESSING AND MANAGEMENT OF UNCERTAINTY IN KNOWLEDGE-BASED SYSTEMS: THEORY AND METHODS, PT 1, Dortmund, GERMANY (2010) | |||||||
2010 |
Jaskowiak PA, Campello RJGB, Covões TF, Hruschka ER, 'A comparative study on the use of correlation coefficients for redundant feature elimination', Proceedings - 2010 11th Brazilian Symposium on Neural Networks, SBRN 2010 (2010) Simplified Silhouette Filter (SSF) is a recently introduced feature selection method that automatically estimates the number of features to be selected. To do so, a sampling strat... [more] Simplified Silhouette Filter (SSF) is a recently introduced feature selection method that automatically estimates the number of features to be selected. To do so, a sampling strategy is combined with a clustering algorithm that seeks clusters of correlated (potentially redundant) features. It is well known that the choice of a similarity measure may have great impact in clustering results. As a consequence, in this application scenario, this choice may have great impact in the feature subset to be selected. In this paper we study six correlation coefficients as similarity measures in the clustering stage of SSF, thus giving rise to several variants of the original method. The obtained results show that, in particular scenarios, some correlation measures select fewer features than others, while providing accurate classifiers. © 2010 IEEE.
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2010 |
da Rosa A, Campello RJGB, Ferreira PAV, Oliveira GHC, Amaral WC, 'Robust Expansion of Uncertain Volterra Kernels into Orthonormal Series', 2010 AMERICAN CONTROL CONFERENCE, Baltimore, MD (2010)
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2009 |
Naldi MC, Fontana A, Campello RJGB, 'Comparison Among Methods for k Estimation in k-means', 2009 9TH INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS DESIGN AND APPLICATIONS, Univ Pisa, Pisa, ITALY (2009)
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2009 |
Horta D, Campello RJGB, 'Fast Evolutionary Algorithms for Relational Clustering', 2009 9TH INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS DESIGN AND APPLICATIONS, Univ Pisa, Pisa, ITALY (2009)
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2009 |
Vendramin L, Campello RJGB, Hruschka ER, 'On the comparison of relative clustering validity criteria', Society for Industrial and Applied Mathematics - 9th SIAM International Conference on Data Mining 2009, Proceedings in Applied Mathematics (2009) Many different relative clustering validity criteria exist that are very useful in practice as quantitative measures for evaluating the quality of data partitions, and new criteri... [more] Many different relative clustering validity criteria exist that are very useful in practice as quantitative measures for evaluating the quality of data partitions, and new criteria have still been proposed from time to time. These criteria are endowed with particular features that may make each of them able to outperform others in specific classes of problems. Then, it is a hard task for the user to choose a specific criterion when he or she faces such a variety of possibilities. For this reason, a relevant issue within the field of cluster analysis consists of comparing the performances of existing validity criteria and, eventually, that of a new criterion to be proposed. In spite of this, there are some conceptual flaws in the comparison paradigm traditionally adopted in the literature. The present paper presents an alternative methodology for comparing clustering validity criteria and uses it to make an extensive comparison of the performances of 4 well-known validity criteria and 20 variants of them over a collection of 142,560 partitions of 324 different data sets of a given class of interest.
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2008 |
Campello RJGB, Hruschka ER, 'A fully sensitive correlation measure for data mining', DATA MINING IX: DATA MINING, PROTECTION, DETECTION AND OTHER SECURITY TECHNOLOGIES, Univ Cadiz, Cadiz, SPAIN (2008)
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2008 |
Vendramin L, Campello RJGB, Hruschka ER, 'A Robust Methodology for Comparing Performances of Clustering Validity Criteria', ADVANCES IN ARTIFICIAL INTELLIGENCE - SBIA 2008, PROCEEDINGS, Federal Univ Bahia, Salvador, BRAZIL (2008)
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2007 |
Alves VS, Campello RJGB, Hruschka ER, 'A fuzzy variant of an Evolutionary Algorithm for Clustering', 2007 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1-4, London, ENGLAND (2007)
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2007 |
Machado JB, Amaral WC, Campello RJGB, 'Design of OBF-TS fuzzy models based on multiple clustering validity criteria', 19TH IEEE INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE, VOL II, PROCEEDINGS, Patras, GREECE (2007)
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2006 |
Reis FAP, Félix IC, Stanzani SL, Campello RJGB, De Castro LN, Senger H, Rosatelli MC, 'A learning object on computational intelligence', Proceedings - Sixth International Conference on Advanced Learning Technologies, ICALT 2006 (2006) This paper presents a Learning Object in the domain of Computational Intelligence that can be used in graduate and undergraduate courses. Additionally, it can be reused in other c... [more] This paper presents a Learning Object in the domain of Computational Intelligence that can be used in graduate and undergraduate courses. Additionally, it can be reused in other contexts and scenarios, such as a distance learning course on Artificial Intelligence. © 2006 IEEE.
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2006 |
Medeiros AV, Amaral WC, Campello RJGB, 'GA optimization of OBF TS fuzzy models with linear and non linear local models', Proceedings of the Ninth Brazilian Symposium on Neural Networks, SBRN'06 (2006) OBF (Orthonormal Basis Function) Fuzzy models have shown to be a promising approach to the areas of nonlinear system identification and control since they exhibit several advantag... [more] OBF (Orthonormal Basis Function) Fuzzy models have shown to be a promising approach to the areas of nonlinear system identification and control since they exhibit several advantages over those dynamic model topologies usually adopted in the literature. Although encouraging application results have been obtained, no automatic procedure had yet been developed to optimize the design parameters of these models. This paper elaborates on the use of a genetic algorithm (GA) especially designed for this task, in which a fitness function based on the Akaike information criterion plays a key role by considering both model accuracy and parsimony aspects. The use of linear (actually affine) and nonlinear local models is also investigated. The proposed methodology is evaluated in the modeling of a real nonlinear magnetic levitation system. © 2006 IEEE.
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2006 |
Alves VS, Campello RJGB, Hruschka ER, 'Towards a fast evolutionary algorithm for clustering', 2006 IEEE Congress on Evolutionary Computation, CEC 2006 (2006) This paper elaborates on the improvement of an evolutionary algorithm for clustering (EAC) introduced in previous work. Four new features are proposed and empirically assessed in ... [more] This paper elaborates on the improvement of an evolutionary algorithm for clustering (EAC) introduced in previous work. Four new features are proposed and empirically assessed in seven datasets, using two fitness functions. Statistical analyses allow concluding that two proposed features lead to significant improvements on the original EAC. Such features have been incorporated into the EAC, resulting in a more computationally efficient algorithm called F-EAC (Fast EAC). We describe as an additional contribution a methodology for evaluating evolutionary algorithms for clustering in such a way that the influence of the fitness function is lessened in the assessment process, what yields analyses specially focused on the evolutionary operators. © 2006 IEEE.
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2006 |
Alves VS, Campello RJGB, Hruschka ER, 'Towards a fast evolutionary algorithm for clustering', 2006 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-6, Vancouver, CANADA (2006)
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2006 |
Medeiros AV, Amaral WC, Campello RJGB, 'GA optimization of generalized OBF TS fuzzy models with global and local estimation approaches', 2006 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1-5, Vancouver, CANADA (2006)
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2005 |
Da Rosa A, Amaral WC, Campello RJGB, 'Choice of free parameters in expansions of discrete-time volterra models using Kautz functions', IFAC Proceedings Volumes (IFAC-PapersOnline) (2005) The present paper involves the approximation of nonlinear systems using Wiener/Volterra models with Kautz orthonormal functions. It focuses on the problem of selecting the free co... [more] The present paper involves the approximation of nonlinear systems using Wiener/Volterra models with Kautz orthonormal functions. It focuses on the problem of selecting the free complex pole which characterizes these functions. The problem is solved by minimizing an upper bound of the error arising from the truncated approximation of Volterra kernels using Kautz functions. An analytical solution for the optimal choice of one of the parameters related to the Kautz pole is thus obtained, with the results valid for any-order Wiener/Volterra models. An example illustrates the application of the mathematical results derived. Copyright © 2005 IFAC.
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2004 |
Hruschka ER, de Castro LN, Campello RJGB, 'Evolutionary algorithms for clustering gene-expression data', FOURTH IEEE INTERNATIONAL CONFERENCE ON DATA MINING, PROCEEDINGS, Brighton, ENGLAND (2004)
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2004 |
Hruschka ER, Campello RJGB, de Castro LN, 'Evolutionary search for optimal fuzzy C-means clustering', 2004 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1-3, PROCEEDINGS, Budapest, HUNGARY (2004)
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2004 |
Campello RJGB, Meleiro LAC, Amaral WC, 'Control of a bioprocess using orthonormal basis function fuzzy models', 2004 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1-3, PROCEEDINGS, Budapest, HUNGARY (2004)
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2004 |
de Castro LN, Hruschka ER, Campello RJGB, 'An evolutionary clustering technique with local search to design RBF neural network classifiers', 2004 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-4, PROCEEDINGS, Budapest, HUNGARY (2004)
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2004 |
Hruschka ER, Campello RJGB, de Castro LN, 'Improving the efficiency of a clustering genetic algorithm', ADVANCES IN ARTIFICIAL INTELLIGENCE - IBERAMIA 2004, Puebla, MEXICO (2004)
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2003 |
Campello RJGB, Favier G, Amaral WC, 'Optimal Expansions of Discrete-Time Volterra Models Using Laguerre Functions', IFAC Proceedings Volumes (IFAC-PapersOnline) (2003) © 2003 International Federation of Automatic Control. This paper is concerned with the optimization of Laguerre bases for the orthonormal series expansion of discrete-time Volterr... [more] © 2003 International Federation of Automatic Control. This paper is concerned with the optimization of Laguerre bases for the orthonormal series expansion of discrete-time Volterra models. Fu and Dumont (1993) approached this problem in the context of linear systems by minimizing an upper bound for the error resulting from the truncated Laguerre expansion of impulse response models, which are equivalent to first-order Volterra models. The present work generalizes the work mentioned above to Volterra models of any order. The main result is the derivation of analytic strict global solutions for the optimal expansion of the Volterra kernels either using an independent Laguerre basis for each kernel or using a common basis for all the kernels.
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2002 |
Meleiro LAC, Campello RJGB, Maciel R, Von Zuben FJ, 'Identification of a multivariate fermentation process using constructive learning', VII BRAZILIAN SYMPOSIUM ON NEURAL NETWORKS, PROCEEDINGS, PERNAMBUCO, BRAZIL (2002)
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2002 |
Campello RJGB, Amaral WC, 'Hierarchical fuzzy relational models: Linguistic interpretation and universal approximation', PROCEEDINGS OF THE 2002 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOL 1 & 2, HONOLULU, HI (2002)
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2002 |
Campello RJGB, Amaral WC, 'Takagi-Sugeno fuzzy models within orthonormal basis function framework and their application to process control', PROCEEDINGS OF THE 2002 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOL 1 & 2, HONOLULU, HI (2002)
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2001 |
Campello RJGB, Amaral WC, Favier G, 'Optimal Laguerre series expansion of discrete volterra models', 2001 European Control Conference, ECC 2001 (2001) © 2001 EUCA. This paper is concerned with the selection of optimal Laguerre bases for the orthonormal series expansion of discrete-time Volterra models. The aim is to minimize the... [more] © 2001 EUCA. This paper is concerned with the selection of optimal Laguerre bases for the orthonormal series expansion of discrete-time Volterra models. The aim is to minimize the number of functions needed to provide a given approximation accuracy, thus simplifying the modeling and control problems associated with these models. This issue was addressed by Fu and Dumont [8] focusing on linear systems, which are equivalent to a first-order Volterra model. The present work is a generalization of the work mentioned above focusing on second-order models. The main result is an analytic strict global solution for the optimal expansion of the second-order Volterra kernel. An example is provided to illustrate some theoretical aspects of the mathematical results presented in the paper.
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2000 |
Campello RJGB, Amaral WC, 'Optimization of hierarchical neural fuzzy models', IJCNN 2000: PROCEEDINGS OF THE IEEE-INNS-ENNS INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOL V, COMO, ITALY (2000)
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1999 |
Oliveira GHC, Campello RJGB, Amaral WC, 'Fuzzy models within orthonormal basis function framework', IEEE International Conference on Fuzzy Systems (1999) This work presents a novel framework for fuzzy modeling of dynamic systems using orthonormal basis functions in the representation of the model input signals. The main objective o... [more] This work presents a novel framework for fuzzy modeling of dynamic systems using orthonormal basis functions in the representation of the model input signals. The main objective of using orthonormal bases is to overcome the task of estimating the order and time delay of the process. The result is a nonlinear moving average fuzzy model which, consequently, has no feedback of prediction errors. Although any technique of fuzzy modeling can be used in the proposed framework, a relational approach is considered. The performance of fuzzy models with orthonormal basis functions is illustrated by examples and the results are compared with those provided by conventional fuzzy models and Volterra models.
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1998 |
Campello RJGB, do Amaral WC, 'Refinement and identification of fuzzy relational models', 1998 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS AT THE IEEE WORLD CONGRESS ON COMPUTATIONAL INTELLIGENCE - PROCEEDINGS, VOL 1-2, ANCHORAGE, AK (1998)
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Show 51 more conferences |
Grants and Funding
Summary
Number of grants | 6 |
---|---|
Total funding | $226,000 |
Click on a grant title below to expand the full details for that specific grant.
20211 grants / $15,000
Exploring Multifaceted Clustering of Complex Electricity Time-Series Data to Support Data-Driven Decision-Making in the Energy Sector (Supplement)$15,000
Funding body: CSIRO - Commonwealth Scientific and Industrial Research Organisation
Funding body | CSIRO - Commonwealth Scientific and Industrial Research Organisation |
---|---|
Project Team | Ricardo J. G. B. Campello, Lachlan O'Neil, Student Un-named |
Scheme | Postgraduate Scholarship |
Role | Lead |
Funding Start | 2021 |
Funding Finish | 2024 |
GNo | |
Type Of Funding | C2110 - Aust Commonwealth - Own Purpose |
Category | 2110 |
UON | N |
20202 grants / $70,000
Scalable Descriptive Models over Extensive Volumes of Distributed Data$40,000
Funding body: Fundacao de Amparo a Pesquisa do Estado de Sao Paulo - FAPESP
Funding body | Fundacao de Amparo a Pesquisa do Estado de Sao Paulo - FAPESP |
---|---|
Project Team | Murilo C. Naldi, Elaine R. F. Paiva, Ricardo Cerri, Ricardo J. G. B. Campello |
Scheme | Regular Research Grants Scheme |
Role | Investigator |
Funding Start | 2020 |
Funding Finish | 2022 |
GNo | |
Type Of Funding | International - Competitive |
Category | 3IFA |
UON | N |
Exploring Multifaceted Clustering of Complex Electricity Time-Series Data to Support Data-Driven Decision-Making in the Energy Sector$30,000
Funding body: CSIRO - Commonwealth Scientific and Industrial Research Organisation
Funding body | CSIRO - Commonwealth Scientific and Industrial Research Organisation |
---|---|
Project Team | Professor Ricardo Gabrielli Barreto Campello, Mr Lachlan O’Neil, Student Un-named |
Scheme | Postgraduate Scholarship |
Role | Lead |
Funding Start | 2020 |
Funding Finish | 2023 |
GNo | G2000708 |
Type Of Funding | C2110 - Aust Commonwealth - Own Purpose |
Category | 2110 |
UON | Y |
20192 grants / $13,000
Data Science Down Under Workshop$7,000
Funding body: AMSI Australian Mathematical Sciences Institute
Funding body | AMSI Australian Mathematical Sciences Institute |
---|---|
Project Team | Ali Eshragh, Fred Roosta, Natalie Thamwattana, Ricardo Gabrielli Barreto Campello, Elizabeth Stojanovski |
Scheme | Small Event Funding |
Role | Investigator |
Funding Start | 2019 |
Funding Finish | 2019 |
GNo | |
Type Of Funding | Aust Competitive - Commonwealth |
Category | 1CS |
UON | N |
Towards Parameter Robust Hierarchical Clustering$6,000
Funding body: Mitacs
Funding body | Mitacs |
---|---|
Project Team | Antonio C. Araujo Neto, Joerg Sander, Ricardo J. G. B. Campello |
Scheme | Globalink Research Awards |
Role | Investigator |
Funding Start | 2019 |
Funding Finish | 2019 |
GNo | |
Type Of Funding | International - Non Competitive |
Category | 3IFB |
UON | N |
20181 grants / $128,000
Machine Learning Approach to Restoration, Prediction and Quality Control of Oceanographic Data from IMOS Moorings$128,000
Funding body: AIMS@JCU (Australian Institute of Marine Sciences & James Cook University)
Funding body | AIMS@JCU (Australian Institute of Marine Sciences & James Cook University) |
---|---|
Project Team | Vinícius S. Alves, Ricardo J. G. B. Campello, Ickjai Lee, Oleg Makarynskyy, Paul Rigby |
Scheme | AIMS@JCU |
Role | Lead |
Funding Start | 2018 |
Funding Finish | 2022 |
GNo | |
Type Of Funding | Not Known |
Category | UNKN |
UON | N |
Research Supervision
Number of supervisions
Current Supervision
Commenced | Level of Study | Research Title | Program | Supervisor Type |
---|---|---|---|---|
2020 | Honours | An Intrinsic Dimensionality Aware Outlier Score | Statistics, Faculty of Science | University of Newcastle | Sole Supervisor |
2019 | Masters | Utilising RandNLA Methods to Solve Markov Decision Processes | M Philosophy (Statistics), College of Engineering, Science and Environment, The University of Newcastle | Co-Supervisor |
2018 | PhD | Machine Learning Algorithms for Time-Series and their Application to Restoration, Prediction and Quality Control of Oceanographic Data (Tentative Title) | Statistics, James Cook University | Principal Supervisor |
2017 | PhD | Transfer and Deep Learning for Pattern Recognition Applications in Agriculture and Cattle Raising (Tentative Title) | Computer Science, University of São Paulo | Sole Supervisor |
Past Supervision
Year | Level of Study | Research Title | Program | Supervisor Type |
---|---|---|---|---|
2020 | PhD | A Framework for Hierarchical Density-Based Clustering Exploration | Computer Science, University of Alberta | Co-Supervisor |
2019 | PhD | Semi-Supervised Learning Approaches with Applications in Medicinal Chemistry | Computer Science, University of São Paulo | Sole Supervisor |
2019 | PhD | Evaluation and Model Selection for Unsupervised Outlier Detection and One-Class Classification | Computer Science, University of São Paulo | Sole Supervisor |
2019 | PhD | Enhancing Recommender Systems by Enrichment with Pre-Processing Approaches Supported by Users' Feedback | Computer Science, University of São Paulo | Principal Supervisor |
2018 | Masters | Pre-Processing Approaches for Collaborative Filtering based on Hierarchical Clustering | Computer Science, University of São Paulo | Co-Supervisor |
2018 | Masters | Clusters Selection from Hierarchies: a Graph-Based Model | Computer Science, University of São Paulo | Sole Supervisor |
2018 | Masters | Fast Algorithms for Hierarchical Density Estimates and their Applications in Data Mining | Computer Science, University of São Paulo | Sole Supervisor |
2017 | Masters | Combinations of Semantic and Term Frequency Similarities for Text Clustering | Computer Science, Universidade Federal De Viçosa | Co-Supervisor |
2016 | Masters | Active, Semi-Supervised Classification based on Multiple Clustering Hierarchies | Computer Science, University of São Paulo | Sole Supervisor |
2016 | Masters | Systematic Comparative Evaluation of Bi-Clustering Techniques | Computer Science, University of São Paulo | Sole Supervisor |
2015 | PhD | On the Evaluation of Clustering Results: Measures, Ensembles, and Gene Expression Data Analysis | Computer Science, University of São Paulo | Principal Supervisor |
2015 | Masters | Study, Evaluation and Comparison of Unsupervised Outlier Detection Techniques | Computer Science, University of São Paulo | Sole Supervisor |
2015 | Masters | On the Internal Evaluation of Unsupervised Outlier Detection | Computer Science, University of São Paulo | Sole Supervisor |
2013 | PhD | Algorithms and Validation Techniques in Multi-Represented Data Clustering, Possibilistic Clustering and Bi-clustering | Computer Science, University of São Paulo | Sole Supervisor |
2012 | Masters | Study and Development of Fuzzy Clustering Algorithms in Centralized and Distributed Scenarios | Computer Science, University of São Paulo | Sole Supervisor |
2011 | PhD | Nonlinear Systems Modeling based on Ladder-Structured Generalized Orthonormal Basis Functions | Electrical Engineering, State University of Campinas | Co-Supervisor |
2011 | PhD | Ensemble Techniques for Centralized and Distributed Clustering | Computer Science, University of São Paulo | Principal Supervisor |
2011 | Masters | A Study of Correlation Coefficients as Proximity Measures for Gene Expression Data | Computer Science, University of São Paulo | Sole Supervisor |
2011 | Masters | Volterra Models: Nonparametric and Robust Identification using Kautz and Generalized Orthonormal Functions | Electrical Engineering, State University of Campinas | Co-Supervisor |
2010 | Masters | Evolutionary Approaches to Relational Data Clustering | Computer Science, University of São Paulo | Sole Supervisor |
2009 | PhD | Identification of Nonlinear Systems using Volterra Models based on Kautz Functions and Generalized Orthonormal Functions | Electrical Engineering, State University of Campinas | Co-Supervisor |
2007 | Masters | Modeling and Predictive Control using Multi-Models | Electrical Engineering, State University of Campinas | Co-Supervisor |
2007 | Masters | F-EAC: A Fast Evolutionary Algorithm for Clustering | Information Systems, Catholic University of Santos | Principal Supervisor |
2006 | Masters | Modeling of Nonlinear Dynamic Systems using Fuzzy Systems, Genetic Algorithms and Orthonormal Basis Functions | Electrical Engineering, State University of Campinas | Co-Supervisor |
2005 | Masters | Expansion of Volterra Models using Kautz Functions | Electrical Engineering, State University of Campinas | Co-Supervisor |
Research Collaborations
The map is a representation of a researchers co-authorship with collaborators across the globe. The map displays the number of publications against a country, where there is at least one co-author based in that country. Data is sourced from the University of Newcastle research publication management system (NURO) and may not fully represent the authors complete body of work.
Country | Count of Publications | |
---|---|---|
Brazil | 105 | |
Canada | 27 | |
Australia | 17 | |
Germany | 12 | |
Denmark | 7 | |
More... |
Professor Ricardo Gabrielli Barreto Campello
Position
Professor
School of Mathematical and Physical Sciences
College of Engineering, Science and Environment
Contact Details
ricardo.campello@newcastle.edu.au | |
Phone | (02) 4921 6762 |
Links |
Research Networks Research Networks |
Office
Room | SR106 |
---|---|
Building | SR Building |
Location | Callaghan University Drive Callaghan, NSW 2308 Australia |