Visual Co-Cluster Assessment with Intuitive Cluster Validation Through Cooccurrence-Sensitive Ordering

Abstract

Co-cluster extraction is a basic approach for summarization of cooccurrence information. This paper proposes a visual assessment technique for co-cluster structure analysis through cooccurrence-sensitive ordering, which realizes the hybrid concept of the coVAT algorithm and distance-sensitive ordering in relational data clustering. Object-item cooccurrence information is first enlarged into an (object + item) × (object + item) cooccurrence data matrix, and then, cooccurrence-sensitive ordering is performed through spectral ordering of the enlarged matrix. Additionally, this paper also consider the intuitive validation of co-cluster structures considering cluster crossing curves, which was adopted in cluster validation with distance-sensitive ordering. The characteristic features of the proposed approach are demonstrated through several numerical experiments including application to social analysis of Japanese prefectural statistics.