MACs: Multi-Attribute Co-Clusters with High Correlation Information

Abstract

In many real-world applications that analyze correlations between two groups of diverse entities, each group of entities can be characterized by multiple attributes. As such, there is a need to co-cluster multiple attributes’ values into pairs of highly correlated clusters. We denote this co-clustering problem as the multi-attribute co-clustering problem. In this paper, we introduce a generalization of the mutual information between two attributes into mutual information between two attribute sets. The generalized formula enables us to use correlation information to discover multi-attribute co-clusters (MACs) . We develop a novel algorithm MACminer to mine MACs with high correlation information from datasets. We demonstrate the mining efficiency of MACminer in datasets with multiple attributes, and show that MACs with high correlation information have higher classification and predictive power, as compared to MACs generated by alternative high-dimensional data clustering and pattern mining techniques.