Autonomous Clustering Characterization for Categorical Data

This paper addresses the problem of cluster characterization by selecting a subset of the most relevant features for each cluster from a categorical dataset in an autonomous way. The proposed autonomous model is based on the Relational Topological Clustering (RTC) associated with a statistical test which allows to detect the most important variables in an automatic way without setting any parameters. The RTC approach is used to build a prototypes matrix which contains continuous variables, where each prototype vector represents correlated categorical data. Thereafter, the statistical ScreeTest is used to detect relevant and correlated features (or modalities) for each prototype. The proposed method requires simple computational techniques and the RTC topology technique is based on the principle of the self-organizing map (SOM) model. This method allows the dimensionality reduction, visualization and cluster characterization simultaneously. Empirical results based on real datasets from the UCI repository, are given and discussed.