Crossed Clustering Method on Symbolic Data Tables

In this paper a crossed clustering algorithm is proposed to partitioning a set of symbolic objects in a fixed number of classes. This algorithm allows, at the same time, to determine a structure (taxonomy) on the categories of the object descriptors. This procedure is an extension of the classical simultaneous clustering algorithms, proposed on binary and contingency tables. It is based on a dynamical clustering algorithm on symbolic objects. The optimized criterion is the Φ2 distance computed between the objects description, given by modal variables (distributions) and the prototypes of the classes, described by marginal profiles of the objects set partitions. The convergence of the algorithm is guaranteed at a stationary value of the criterion, in correspondence of the best partition of the symbolic objects in r classes and the best partition of the symbolic descriptors in c groups. An application on web log data has allowed to validate the procedure and suggest it as an useful tool in the Web Usage Mining context. Fulltext Preview