An Effective Approach on Overlapping Structures Discovery for Co-clustering

Abstract

Co-clustering, which explores the inter-connected structures between objects and features simultaneously, has drawn much attention in the past decade. Most existing methods for co-clustering focus on partition-based approaches, which assume that each entry of the data matrix can only be assigned to one cluster. However, in the real world applications, the cluster structures can potential be overlapping. In this paper, we propose a novel overlapping co-clustering method by introducing the density guided principle for discriminative features (objects) identification. This is done by simultaneously finding the non-overlapping blocks. Based on the discovered blocks, an effective strategy is utilized to select the features (objects), which can discriminate the specified object (feature) cluster from other object (feature) clusters. Finally, according to the discriminative features (objects), a novel overlapping method, OPS, is proposed. Experimental studies on both synthetic and real-world data sets demonstrate the effectiveness and efficiency of the proposed OPS method.