PARTCAT: A Subspace Clustering Algorithm for High Dimensional Categorical Data

A new subspace clustering algorithm, PARTCAT, is proposed to cluster high dimensional categorical data. The architecture of PARTCAT is based on the recently developed neural network architecture PART, and a major modification is provided in order to deal with categorical attributes. PARTCAT requires less number of parameters than PART, and in particular, PARTCAT does not need the distance parameter that is needed in PART and is intimately related to the similarity in each fixed dimension. Some simulations using real data sets to show the performance of PARTCAT are provided.