Categorical Data Clustering: A Correlation-Based Approach for Unsupervised Attribute Weighting

Abstract

The interest in attribute weighting, in clustering tasks, have been increasing in the last years. However, few attempts have been made to apply automated attribute weighting to categorical data clustering. Most of the existing approaches computes the weights based on the frequency of the mode category or according to the average distance of data objects from the mode of a cluster. In this paper, we adopt a different approach, investigating how to use the correlation among categorical attributes for measuring their relevancies in clustering tasks. As a result, we propose a correlation-based attribute weighting approach for categorical attributes.