Minimum Error Classification Clustering

Abstract

Clustering is the problem of identifying the distribution of patterns and intrinsic correlations in large data sets by partitioning the data points into similarity classes. In this paper, we study on the problem of clustering categorical data, where data objects are made up of non-numerical attributes. We propose MECC (Minimum Error Classification Clustering), an alternative technique for categorical data clustering using VPRS taking into account minimum error classification. The technique is implemented in MATLAB. Experimental results on two benchmark UCI datasets show that MECC technique is better than the baseline categorical data clustering techniques with respect to selecting the clustering attribute.