Palestrante: Marcilio de Souto, Laboratoire d'Informatique Fondamentale
d'Orléans/Université d'Orléans, France

Currently, more and more data are collected from multiple sources or
represented by multiple views (e.g., text, video, images, biological
data, among others). In this context, clustering techniques are often
required to be able to provide several possibilities for analyzing the
data. As a consequence, in recent years, the interdisciplinary
research topic on Multiple Clusterings has drawn significant attention
of the data mining community. For example, there has been an
increasing interest in discovering multiple clustering solutions from
very high dimensional and complex databases. Likewise, there have
been a great interest in searching for a consensus among clusterings
representing different views of the data. In this talk, we will
approach problems in areas such as cluster ensemble, multi-objective
clustering and multi-view clustering.

Bio: Marcilio de Souto received a B.Sc. degree in Computer Science
from the Federal University of Rio Grande do Norte (UFRN), Brazil, in
1992, the M.Sc. degree in Computer Science from the Federal University
of Pernambuco (UFPE), Brazil, in 1995, and the Ph.D. degree in
Electrical Engineering (Artificial Intelligence) from the Imperial
College (London), UK, in 1999. He worked as a visiting professor at
the Center of Informatics (CIn) of the UFPE for three years and, from
2002, he stayed one year as a researcher at the Institute of
Mathematics and Computing of the University of São Paulo at São Carlos
(Brazil). In 2004, he joined, as an associate professor, the
Department of Informatics and Applied Mathematics of the UFRN. He
spent one year as a visiting researcher at the Department of
Computational Biology of the Max Planck Institute of Molecular
Genetics, Berlin, Germany. Currently, he is a full professor at the
Laboratoire d'Informatique Fondamentale d'Orléans (LIFO) at the
University of Orléans (France). His main research topic is Machine
Learning, with focus on Cluster Analysis, Hybrid Intelligent Systems,
and Bioinformatics.