A heuristic-based fuzzy co-clustering algorithm for categorization of high-dimensional data

Abstract

Fuzzy co-clustering is a technique that performs simultaneous fuzzy clustering of objects and features. It is known to be suitable for categorizing high-dimensional data, due to its dynamic dimensionality reduction mechanism achieved through simultaneous feature clustering. We introduce a new fuzzy co-clustering algorithm called Heuristic Fuzzy Co-clustering with the Ruspini's condition (HFCR), which addresses several issues in some prominent existing fuzzy co-clustering algorithms. Among these issues are the performance on data sets with overlapping feature clusters and the unnatural representation of feature clusters. The key idea behind HFCR is the formulation of the dual-partitioning approach for fuzzy co-clustering, replacing the existing partitioning-ranking approach. HFCR adopts an efficient and practical heuristic method that can be shown to be more robust than our earlier effort for the dual-partitioning approach. We explain the proposed algorithm in details and provide an analytical study on its advantages. Experimental results on 10 large benchmark document data sets confirm the effectiveness of the new algorithm.