This workshop is concerned with the study of cluster analysis through machine learning, game theory and social choice theory.
Cluster analysis is a central research topic in Machine Learning. It deals with the problem of grouping a set of objects in such a way that objects in one group are more `related' to each other than to objects in the other groups. In general it is not clear what is a correct definition of clustering of a given set of objects. Consequently, different perspectives on cluster analysis have emerged yielding a wealth of approaches and algorithms.
The research agenda in game theory and social choice theory is highly relevant for cluster analysis and there are some interesting parallels on a methodological level. The primary goal of this workshop is to create a forum for discussing common research objectives and to unravel and study in depth these striking, not well-known, parallels in these research areas. We hope that in this way the researchers working in game theory, social choice theory and machine learning will profit from each other through the study of cluster analysis. This could lead to new research directions and to new advances in all three fields.