Mutual information based weighted clustering for mixed attributes

Abstract

There exists large number of clustering algorithms either for numeric or for categorical data sets. There are relatively less algorithms for clustering mixed attributes. This paper proposes Mutual Information based Weighted Clustering for Mixed Attributes (MI-WCMA) based on euclidean distance for numeric attributes, distance measure based on similarity for categorical attributes using rough sets and weights for features based on average mutual information. The metrics accuracy, silhouette width and kappa co-efficient are used for evaluation and comparison with existing algorithms.