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 a 16:00 - Lunch
16:00 a 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.