Spectral co-clustering ensemble

Abstract

The goal of co-clustering is to simultaneously cluster the rows and columns of an input data matrix. It overcomes several limitations associated with traditional clustering methods by allowing automatic discovery of similarity based on a subset of attributes. However, different co-clustering models usually produce very distinct results since each algorithm has its own bias due to the optimization of different criteria. The idea of combining different co-clustering results emerged as an alternative approach for improving the performance of co-clustering algorithms. Similar to clustering ensembles, co-clustering ensembles provide a framework for combining multiple base co-clusterings of a dataset to generate a stable and robust consensus co-clustering result. In this paper, a novel co-clustering ensemble algorithm named spectral co-clustering ensemble (SCCE) is presented. SCCE performs ensemble tasks on base row clusters and column clusters of a dataset simultaneously, and obtains an optimization co-clustering result. Meanwhile, SCCE is a matrix decomposition based approach which can be formulated as a bipartite graph partition problem and solve it efficiently with the selected eigenvectors. To the best of our knowledge, this is the first work on using spectral algorithm for co-clustering ensemble. Extensive experiments on benchmark datasets demonstrate the effectiveness of the proposed method. Our study also shows that SCCE has some favorable merits compared with many state of the art methods.