Professor Ricardo Gabrielli Barreto Campello

Professor Ricardo Gabrielli Barreto Campello

Professor

School of Mathematical and Physical Sciences

Career Summary

Biography

Professor Ricardo Campello's expertise ranges across the disciplines of electrical and computer engineering, computer science, and applied mathematics/statistics. He has been teaching and doing research across these disciplines for over 15 years, in Brazil, France, Canada, and Australia. He has successfully advised a number of postgraduate students (over 25 MSc/PhD students completed or in progress) as well as undergraduate students (15+ capstone projects and research internships), many of which supported by research grants. Prior to his move to Australia in late 2016, Ricardo continuously secured funding to support his research group over the years, including highly skilled personnel (post-doctoral fellows and a visiting professor on sabbatical leave). In Australia, Ricardo has been mainly involved with the online learning business, particularly the design and development of new online postgraduate programs in the area of data science and analytics.

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, and regression. 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, and 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), oceanography (time-series mining), and automation (control 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 110+ research papers in scholarly journals, book chapters, and peer-reviewed conference proceedings, with over 3600 citations detected by the Google Scholar database and a h-index = 29 as of October/2018 (h-index = 23 according to Scopus).

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 a 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 programme. 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.

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

  • Applied Mathematics
  • Applied Statistics
  • Computer Science
  • Data Mining and Machine Learning
  • Data Science
  • Electrical Engineering
  • Statistical Learning

Languages

  • Portuguese (Mother)
  • English (Fluent)

Fields of Research

Code Description Percentage
080109 Pattern Recognition and Data Mining 50
010401 Applied Statistics 50

Professional Experience

UON Appointment

Title Organisation / Department
Professor University of Newcastle
School of Mathematical and Physical Sciences
Australia

Academic appointment

Dates Title Organisation / Department
1/07/2018 - 30/06/2021 Adjunct Professor James Cook University
College of Science and Engineering
Australia
1/07/2017 - 30/06/2020 Adjunct Professor University of Alberta
Department of Computing Science
Canada
1/11/2016 - 30/06/2018 Professor James Cook University
College of Science and Engineering
Australia
1/08/2011 - 31/07/2013 Visiting Professor University of Alberta
Department of Computing Science
Canada
1/07/2011 - 30/10/2016 Associate Professor University of São Paulo
Institute of Mathematics and Computer Science
Brazil
1/01/2007 - 30/06/2011 Assistant Professor University of São Paulo
Institute of Mathematics and Computer Science
Brazil
1/03/2005 - 28/02/2019 Merit Scholar Brazilian National Research Council (CNPq)
Brazil
1/07/2003 - 20/12/2006 Assistant Professor Catholic University of Santos
Postgraduate Program in Informatics
Brazil
1/04/2003 - 31/12/2006 Research Associate and Guest Lecturer State University of Campinas
School of Electrical and Computer Engineering
Brazil
1/10/2002 - 31/01/2003 Post-Doctoral Research Fellow University of Nice Sophia Antipolis
Laboratoire D´Informatique, Signaux et Systèmes (I3S)
France

Membership

Dates Title Organisation / Department
1/01/2015 -  Associate Editor Computational Intelligence Journal (Wiley)
United States

Awards

Award

Year Award
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)
Edit

Publications

For publications that are currently unpublished or in-press, details are shown in italics.


Chapter (6 outputs)

Year Citation Altmetrics Link
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)
DOI 10.1007/978-1-4614-7163-9_356-1
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.

DOI 10.1007/978-3-319-09259-1_5
Citations Scopus - 2
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.

DOI 10.1007/978-3-642-01088-0_8
Citations Scopus - 8
2008 Naldi M, Carvalho A, Gabrielli Barreto Campello RJ, Hruschka E, 'Genetic Clustering for Data Mining', Soft Computing for Knowledge Discovery and Data Mining, Springer Science & Business Media, Boston, MA 95-114 (2008)
DOI 10.1007/978-0-387-69935-6_5
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.

DOI 10.1007/978-3-540-73297-6_12
Citations Scopus - 5
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)
DOI 10.1142/9781848161467_0008
Citations Web of Science - 1
Show 3 more chapters

Journal article (47 outputs)

Year Citation Altmetrics Link
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]
DOI 10.1016/j.eswa.2018.08.020
2018 Neto ACA, Nascimento MA, Sander J, Campello RJGB, 'MustaCHE: A Multiple Clustering Hierarchies Explorer', PROCEEDINGS OF THE VLDB ENDOWMENT, 11 2058-2061 (2018)
DOI 10.14778/3229863.3236259
2018 Jaskowiak PA, Costa IG, Campello RJGB, 'Clustering of RNA-Seq samples: Comparison study on cancer data', METHODS, 132 42-49 (2018)
DOI 10.1016/j.ymeth.2017.07.023
Citations Scopus - 1Web of Science - 1
2017 Oliveira GV, Coutinho FP, Campello RJGB, Naldi MC, 'Improving k-means through distributed scalable metaheuristics', NEUROCOMPUTING, 246 45-57 (2017)
DOI 10.1016/j.neucom.2016.07.074
Citations Scopus - 9Web of Science - 5
2017 Padilha VA, Campello RJGB, 'A systematic comparative evaluation of biclustering techniques', BMC BIOINFORMATICS, 18 (2017)
DOI 10.1186/s12859-017-1487-1
Citations Scopus - 10Web of Science - 8
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)
DOI 10.1007/s10115-015-0851-6
Citations Scopus - 7Web of Science - 6
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)
DOI 10.1007/s10618-015-0444-8
Citations Scopus - 56Web of Science - 34
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)
DOI 10.1109/TAC.2015.2423912
Citations Scopus - 1Web of Science - 1
2015 Naldi MC, Campello RJGB, 'Comparison of distributed evolutionary k-means clustering algorithms', NEUROCOMPUTING, 163 78-93 (2015)
DOI 10.1016/j.neucom.2014.07.083
Citations Scopus - 15Web of Science - 13
2015 Horta D, Campello RJGB, 'Comparing Hard and Overlapping Clusterings', JOURNAL OF MACHINE LEARNING RESEARCH, 16 2949-2997 (2015)
Citations Scopus - 4Web of Science - 3
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)
DOI 10.1145/2733381
Citations Scopus - 59Web of Science - 38
2014 Naldi MC, Campello RJGB, 'Evolutionary k-means for distributed data sets', NEUROCOMPUTING, 127 30-42 (2014)
DOI 10.1016/j.neucom.2013.05.046
Citations Scopus - 28Web of Science - 25
2014 Jaskowiak PA, Campello RJGB, Costa IG, 'On the selection of appropriate distances for gene expression data clustering', BMC BIOINFORMATICS, 15 (2014)
DOI 10.1186/1471-2105-15-S2-S2
Citations Scopus - 33Web of Science - 31
2014 Horta D, Campello RJGB, 'Similarity Measures for Comparing Biclusterings', IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, 11 942-954 (2014)
DOI 10.1109/TCBB.2014.2325016
Citations Scopus - 5Web of Science - 4
2013 Naldi MC, Carvalho ACPLF, Campello RJGB, 'Cluster ensemble selection based on relative validity indexes', DATA MINING AND KNOWLEDGE DISCOVERY, 27 259-289 (2013)
DOI 10.1007/s10618-012-0290-x
Citations Scopus - 29Web of Science - 20
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)
DOI 10.1007/s10618-013-0311-4
Citations Scopus - 18Web of Science - 10
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)
DOI 10.1109/TCBB.2013.9
Citations Scopus - 18Web of Science - 15
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.

DOI 10.1007/s13278-013-0132-x
Citations Scopus - 5
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)
DOI 10.1109/TSMCB.2012.2217323
Citations Scopus - 16Web of Science - 9
2012 Horta D, Campello RJGB, 'Automatic aspect discrimination in data clustering', PATTERN RECOGNITION, 45 4370-4388 (2012)
DOI 10.1016/j.patcog.2012.05.011
Citations Scopus - 11Web of Science - 8
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)
DOI 10.1109/TFUZZ.2011.2175400
Citations Scopus - 59Web of Science - 51
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)
DOI 10.1109/TCBB.2012.115
Citations Scopus - 1
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.

DOI 10.1504/IJMIC.2012.046691
Citations Scopus - 8
2011 Horta D, de Andrade IC, Campello RJGB, 'Evolutionary fuzzy clustering of relational data', THEORETICAL COMPUTER SCIENCE, 412 5854-5870 (2011)
DOI 10.1016/j.tcs.2011.05.039
Citations Scopus - 10Web of Science - 9
2011 Naldi MC, Campello RJGB, Hruschka ER, Carvalho ACPLF, 'Efficiency issues of evolutionary k-means', APPLIED SOFT COMPUTING, 11 1938-1952 (2011)
DOI 10.1016/j.asoc.2010.06.010
Citations Scopus - 59Web of Science - 39
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.

DOI 10.1504/IJMIC.2011.042346
Citations Scopus - 14
2010 Campello RJGB, 'Generalized external indexes for comparing data partitions with overlapping categories', PATTERN RECOGNITION LETTERS, 31 966-975 (2010)
DOI 10.1016/j.patrec.2010.01.002
Citations Scopus - 25Web of Science - 19
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.

DOI 10.1002/sam.10080
Citations Scopus - 119
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)
DOI 10.1109/TAC.2009.2031721
Citations Scopus - 16Web of Science - 10
2009 Campello RJGB, Hruschka ER, 'On comparing two sequences of numbers and its applications to clustering analysis', INFORMATION SCIENCES, 179 1025-1039 (2009)
DOI 10.1016/j.ins.2008.11.028
Citations Scopus - 28Web of Science - 18
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)
DOI 10.1109/TSMCC.2008.2007252
Citations Scopus - 404Web of Science - 295
2009 Campello RJGB, Hruschka ER, Alves VS, 'On the efficiency of evolutionary fuzzy clustering', JOURNAL OF HEURISTICS, 15 43-75 (2009)
DOI 10.1007/s10732-007-9059-6
Citations Scopus - 37Web of Science - 28
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)
DOI 10.1080/00207170701599070
Citations Scopus - 8Web of Science - 6
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)
DOI 10.1016/j.automatica.2006.12.007
Citations Scopus - 33Web of Science - 24
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.

Citations Scopus - 3
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.

Citations Scopus - 3
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)
DOI 10.1016/j.patrec.2006.11.010
Citations Scopus - 90Web of Science - 69
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)
DOI 10.1016/j.automatica.2005.12.003
Citations Scopus - 22Web of Science - 17
2006 Hruschka ER, Campello RJGB, de Castro LN, 'Evolving clusters in gene-expression data', INFORMATION SCIENCES, 176 1898-1927 (2006)
DOI 10.1016/j.ins.2005.07.015
Citations Scopus - 103Web of Science - 86
2006 Campello RJGB, Hruschka ER, 'A fuzzy extension of the silhouette width criterion for cluster analysis', FUZZY SETS AND SYSTEMS, 157 2858-2875 (2006)
DOI 10.1016/j.fss.2006.07.006
Citations Scopus - 97Web of Science - 79
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)
DOI 10.1080/08839510600941379
Citations Scopus - 6Web of Science - 5
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)
DOI 10.1109/TFUZZ.2006.876365
Citations Scopus - 20Web of Science - 18
2004 Campello RJGB, Favier G, do Amaral WC, 'Optimal expansions of discrete-time Volterra models using Laguerre functions', AUTOMATICA, 40 815-822 (2004)
DOI 10.1016/j.automatica.2003.11.016
Citations Scopus - 78Web of Science - 61
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)
DOI 10.1016/S0009-2509(03)00309-9
Citations Scopus - 20Web of Science - 14
2003 Campello RJGB, Amaral WC, 'Towards true linguistic modelling through optimal numerical solutions', INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE, 34 139-157 (2003)
DOI 10.1080/0020772031000115524
Citations Scopus - 12Web of Science - 8
2001 Campello RJGB, Amaral WC, 'Modeling and linguistic knowledge extraction from systems using fuzzy relational models', FUZZY SETS AND SYSTEMS, 121 113-126 (2001)
DOI 10.1016/S0165-0114(99)00175-X
Citations Scopus - 21Web of Science - 17
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)
DOI 10.1142/S0218488598000033
Citations Scopus - 3Web of Science - 3
Show 44 more journal articles

Conference (52 outputs)

Year Citation Altmetrics Link
2018 Da Costa AF, Manzato MG, Campello RJGB, 'CoRec: A co-training approach for recommender systems', Proceedings of the ACM Symposium on Applied Computing (2018)

© 2018 ACM. In Recommender Systems, a large amount of labeled data must be available beforehand to obtain good predictions. However, labeled data are often limited and expensive t... [more]

© 2018 ACM. In Recommender Systems, a large amount of labeled data must be available beforehand to obtain good predictions. However, labeled data are often limited and expensive to obtain, since labeling typically requires human expertise, time, and labor. This paper proposes a framework, named CoRec, which is based on a co-training approach that drives two recommenders to agree with each other's predictions to generate their own. We used three publicly available datasets from movies, jokes and books domains, as well as two well-known recommender algorithms, to demonstrate the efficiency of the approach under different configurations. The experiments show that better accuracy can be obtained when recommender algorithms are simultaneously co-trained from multiple views to make predictions.

DOI 10.1145/3167132.3167209
Citations Scopus - 1
2018 Da Costa A, Fressato E, Neto F, Manzato M, Campello R, 'Case recommender: A flexible and extensible python framework for recommender systems', RecSys 2018 - 12th ACM Conference on Recommender Systems (2018)

© 2018 Copyright held by the owner/author(s). This paper presents a polished open-source Python-based recommender framework named Case Recommender, which provides a rich set of co... [more]

© 2018 Copyright held by the owner/author(s). This paper presents a polished open-source Python-based recommender framework named Case Recommender, which provides a rich set of components from which developers can construct and evaluate customized recommender systems. It implements well-known and state-of-the-art algorithms in rating prediction and item recommendation scenarios. The main advantage of the Case Recommender is the possibility to integrate clustering and ensemble algorithms with recommendation engines, easing the development of more accurate and efficient approaches.

DOI 10.1145/3240323.3241611
2018 Gertrudes JC, Sander J, Zimek A, Campello RJGB, 'A unified framework of density-based clustering for semi-supervised classification', ACM International Conference Proceeding Series (2018)

© 2018 Copyright held by the owner/author(s). Semi-supervised classification is drawing increasing attention in the era of big data, as the gap between the abundance of cheap, aut... [more]

© 2018 Copyright held by the owner/author(s). Semi-supervised classification is drawing increasing attention in the era of big data, as the gap between the abundance of cheap, automatically collected unlabeled data and the scarcity of labeled data that are laborious and expensive to obtain is dramatically increasing. In this paper, we introduce a unified framework for semi-supervised classification based on building-blocks from density-based clustering. This framework is not only efficient and effective, but it is also statistically sound. Experimental results on a large collection of datasets show the advantages of the proposed framework.

DOI 10.1145/3221269.3223037
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)
DOI 10.1109/ICDM.2017.127
Citations Scopus - 3Web of Science - 2
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.

DOI 10.1145/2851613.2851923
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.

DOI 10.1145/2976796.2976852
Citations Scopus - 2
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), Montral, CANADA (2016)
DOI 10.1109/DSAA.2016.8
Citations Scopus - 4Web of Science - 3
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)
DOI 10.1109/DSAA.2016.9
Citations Scopus - 1Web of Science - 1
2015 Jaskowiak PA, Campello RJGB, 'A Cluster Based Hybrid Feature Selection Approach', 2015 BRAZILIAN CONFERENCE ON INTELLIGENT SYSTEMS (BRACIS 2015), Natal, BRAZIL (2015)
DOI 10.1109/BRACIS.2015.14
Citations Scopus - 4Web of Science - 4
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)
DOI 10.1145/2791347.2791352
Citations Scopus - 5Web of Science - 2
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)
Citations Scopus - 4
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.

DOI 10.1145/2618243.2618257
Citations Scopus - 4
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.

DOI 10.1137/1.9781611973440.96
Citations Scopus - 19
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.

DOI 10.5441/002/edbt.2014.31
Citations Scopus - 2
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.

DOI 10.1145/2484838.2484844
Citations Scopus - 4
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.

DOI 10.1145/2487575.2487676
Citations Scopus - 55
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.

DOI 10.1007/978-3-642-37456-2_14
Citations Scopus - 108
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)
DOI 10.1109/BRACIS.2013.20
Citations Scopus - 3Web of Science - 1
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)
Citations Scopus - 15Web of Science - 7
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.

DOI 10.1109/SBRN.2012.11
Citations Scopus - 4
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.

DOI 10.1007/978-3-642-31927-3_11
Citations Scopus - 8
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)
Citations Scopus - 8Web of Science - 6
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.

DOI 10.1109/MED.2011.5983003
Citations Scopus - 1
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.

DOI 10.1109/ISDA.2011.6121709
Citations Scopus - 1
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.

DOI 10.1109/SBRN.2010.11
Citations Scopus - 6
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)
Citations Scopus - 1
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)
DOI 10.1109/ISDA.2009.78
Citations Scopus - 11Web of Science - 7
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)
DOI 10.1109/ISDA.2009.80
Citations Scopus - 7Web of Science - 5
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.

Citations Scopus - 32
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)
Citations Scopus - 1Web of Science - 1
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)
Citations Scopus - 4Web of Science - 3
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)
Citations Scopus - 6Web of Science - 1
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)
DOI 10.1109/ICTAI.2007.87
Citations Scopus - 8Web of Science - 3
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.

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.

DOI 10.1109/SBRN.2006.20
Citations Scopus - 5
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.

Citations Scopus - 32
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)
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)
DOI 10.1109/FUZZY.2006.1681955
Citations Scopus - 5
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.

Citations Scopus - 2
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)
DOI 10.1109/ICDM.2004.10073
Citations Scopus - 45Web of Science - 13
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)
Citations Scopus - 12Web of Science - 1
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)
Citations Scopus - 6Web of Science - 4
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)
Citations Scopus - 14Web of Science - 2
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)
Citations Scopus - 25Web of Science - 17
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)
DOI 10.1109/SBRN.2002.1181429
Citations Scopus - 1Web of Science - 1
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)
Citations Scopus - 5Web of Science - 3
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)
Citations Scopus - 12Web of Science - 5
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.

Citations Scopus - 10
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)
DOI 10.1109/IJCNN.2000.861427
Citations Scopus - 10Web of Science - 1
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.

Citations Scopus - 17
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)
Citations Scopus - 1
Show 49 more conferences
Edit

Grants and Funding

Summary

Number of grants 1
Total funding $128,000

Click on a grant title below to expand the full details for that specific grant.


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
Edit

Research Supervision

Number of supervisions

Completed21
Current6

Current Supervision

Commenced Level of Study Research Title Program Supervisor Type
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
2015 PhD TBD Computer Science, University of Alberta Co-Supervisor
2015 PhD Clustering-Based Recommender Systems with Multiple User Interactions (Tentative Title) Computer Science, University of São Paulo Principal Supervisor
2015 PhD Evaluation, Model Selection, and Unsupervised Outlier Detection in Full Data Space and Subspaces (Tentative Title) Computer Science, University of São Paulo Sole Supervisor
2014 PhD Semi-Supervised Learning Algorithms with Applications to Medicinal Chemistry (Tentative Title) Computer Science, University of São Paulo Sole Supervisor

Past Supervision

Year Level of Study Research Title Program Supervisor Type
2018 Masters Clusters Selection from Hierarchies: a Graph-Based Model Computer Science, University of São Paulo Sole Supervisor
2018 Masters Pre-Processing Approaches for Collaborative Filtering based on Hierarchical Clustering Computer Science, University of São Paulo Co-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 Systematic Comparative Evaluation of Bi-Clustering Techniques Computer Science, University of São Paulo Sole Supervisor
2016 Masters Active, Semi-Supervised Classification based on Multiple Clustering Hierarchies 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 On the Internal Evaluation of Unsupervised Outlier Detection Computer Science, University of São Paulo Sole Supervisor
2015 Masters Study, Evaluation and Comparison of Unsupervised Outlier Detection Techniques 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 Ensemble Techniques for Centralized and Distributed Clustering Computer Science, University of São Paulo Principal Supervisor
2011 PhD Nonlinear Systems Modeling based on Ladder-Structured Generalized Orthonormal Basis Functions Electrical Engineering, State University of Campinas Co-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 F-EAC: A Fast Evolutionary Algorithm for Clustering Information Systems, Catholic University of Santos Principal Supervisor
2007 Masters Modeling and Predictive Control using Multi-Models Electrical Engineering, State University of Campinas Co-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
Edit

Professor Ricardo Gabrielli Barreto Campello

Position

Professor
School of Mathematical and Physical Sciences
Faculty of Science

Contact Details

Email ricardo.campello@newcastle.edu.au
Phone (02) 4921 6762
Links Research Networks
Research Networks

Office

Room SR112
Building SR Building
Location Callaghan
University Drive
Callaghan, NSW 2308
Australia
Edit