Rough Set Approach for Categorical Data Clustering
Clustering categorical data is an integral part of data mining and has attracted much attention recently. In this paper, the authors focus their discussion on the rough set theory for categorical data clustering. They propose MADE (Maximal Attributes DEpendency), an alternative technique for categorical data clustering using rough set theory taking into account maximum attributes dependencies degree in categorical-valued information systems. Experimental results on two benchmark UCI datasets show that MADE technique is better with the baseline categorical data clustering technique with respect to computational complexity and clusters purity.