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WWW HAIS'09

Evolutionary Algorithms for Clustering

Tutorial Outline

This tutorial presents an overview of evolutionary algorithms designed for clustering tasks. It tries to reflect the profile of this area by focusing more on those subjects that have been given more importance in the literature. In this context, most of the tutorial will be devoted to partitional algorithms that look for hard clusterings of data, though overlapping approaches are also covered. The tutorial provides an up-to-date overview that is fully devoted to evolutionary algorithms for clustering and comprises advanced topics, like multi-objective and ensemble-based evolutionary clustering. It also provides a taxonomy that highlights some important aspects in the context of evolutionary data clustering, namely, fixed or variable number of clusters, cluster-oriented or non-oriented operators, context-sensitive or context-insensitive operators, guided or unguided operators, binary, integer or real encodings, centroid-based, medoid-based, label-based, tree-based or graph-based representations, among others. Particular emphasis will be given to hybrid evolutionary algorithms that make use of popular clustering algorithms, such as k-means and fuzzy c-means, which are widely used in practice.

Target groups of attendees:

The tutorial is designed to serve researchers, developers, graduate students and others interested in state-of-the art evolutionary algorithms for clustering. Practitioners who want a concise, intuitive overview of this research area should also attend. Attendees are assumed to have a common interest in clustering and/or evolutionary algorithms, but with diverse backgrounds in fields such as engineering, computer science, artificial intelligence, pattern recognition, and data analysis.

Program:

14:00 to 16:00 - Lunch
16:00 to 21:00 - Tutorial
21:30 - IWANN Welcome reception

Instructors

Eduardo R. Hruschka, Ricardo J. G. B. Campello, André C. P. L. F. de Carvalho.

Reference paper:

This tutorial is partially (but not strictly) based on the following paper and references therein:

Hruschka, E. R., Campello, R. J. G. B., Freitas, A. A., de Carvalho, A. C. P. L. F., A Survey of Evolutionary Algorithms for Clustering, IEEE Transactions on Systems, Man and Cybernetics - Part C: Applications and Reviews, v. 39, n. 2, pp. 133-155, 2009.

Content and Outline

• Basic concepts;
• k-means algorithm;
• Fuzzy C-Means (FCM) algorithm.

• Overview of Evolutionary Algorithms for Hard Clustering:
  • Encoding schemes;
  • Operators (crossover and mutation);
  • Typical fitness functions;
  • Selection;
  • Initialization.
• Overview of Evolutionary Algorithms for Fuzzy Clustering:
  • Extension of algorithms conceived to evolve hard partitions to the fuzzy domain;
  • Typical fitness functions.

• Combining evolutionary search with basic clustering algorithms (e.g., k-means and FCM);
• Computational efficiency issues;
• Illustrative experiments.

• Multiobjective evolutionary clustering;
• Ensemble-based evolutionary clustering.

Instructors Curriculum

Prof. Eduardo R. Hruschka

Eduardo R. Hruschka received his B.Sc. degree in Civil Engineering from Federal University of Paraná, Brazil, in 1995, and his M.Sc. and Ph.D. degrees in Computational Systems from Federal University of Rio de Janeiro in 1998 and 2001, respectively. He is currently assistant professor of Computer Sciences of the University of São Paulo (USP) at São Carlos, Brazil. His primary research interests are in data mining, with particular emphasis on clustering algorithms, evolutionary computation, feature selection, missing values imputation, and artificial neural networks. He has authored or coauthored more than 50 publications in peer-reviewed journals, book chapters, and conference proceedings. Dr. Hruschka has been a reviewer for journals such as IEEE TSMC, IEEE TKDE, IEEE TEC, Information Sciences, Journal of Heuristics, and Bioinformatics. He has also been a member of the Program Committee of several international conferences.

Prof. Ricardo J. G. B. Campello

Ricardo J. G. B. Campello received the BSc degree in Electronics Engineering from State University of São Paulo (Unesp), in 1994, and the MSc and Ph.D. degrees in Electrical Engineering from the School of Electrical and Computer Engineering of the State University of Campinas (Unicamp), in 1997 and 2002, respectively. Since 2007 he is with the Department of Computer Sciences of the University of São Paulo (USP) at São Carlos. He has (co-)authored more than 50 international publications in peer-reviewed journals, book chapters, and conference proceedings. He has also served regularly as a reviewer for several international journals, such as Information Sciences, Fuzzy Sets and Systems, Automatica, IEEE TFS, IEEE TSMC, and IEEE TAC. His current research interests fall primarily into the areas of Soft Computing, Machine Learning, Data Mining and Dynamic Systems Identification/Control.

Prof. André C. P. L. F. de Carvalho

André C. P. L. F. de Carvalho received his B.Sc. and M.Sc. degrees in Computer Science from the Universidade Federal de Pernambuco, Brazil. He received his Ph.D. degree in Electronic Engineering from the University of Kent, UK. Prof. André de Carvalho is Full Professor at the Department of Computer Science, Universidade de São Paulo, Brazil. He has published around 60 Journal and 200 Conference refereed papers. He has been involved in the organization of several conferences and journal special issues. His main interests are Machine Learning, Data Mining, Bioinformatics, Evolutionary Computation, Bioinspired Computing and Hybrid Intelligent Systems.

Program

Facultad de Ciencias - University of Salamanca
Plaza de la Merced s/n, - Auditorium

9th June 2009

14:00 a 16:00 - Lunch
16:00 a 21:00 - Tutorial
21:30 - IWANN Welcome reception

Registration

Contact