A Kernel Probabilistic Model for Semi-supervised Co-clustering Ensemble

Abstract

Abstract Co-clustering is used to analyze the row and column clusters of a dataset, and it is widely used in recommendation systems. In general, different co-clustering models often obtain very different results for a dataset because each algorithm has its own optimization criteria. It is an alternative way to combine different co-clustering results to produce a final one for improving the quality of co-clustering. In this paper, a semi-supervised co-clustering ensemble is illustrated in detail based on semi-supervised learning and ensemble learning. A semi-supervised co-clustering ensemble is a framework for combining multiple base co-clusterings and the side information of a dataset to obtain a stable and robust consensus co-clustering. First, the objective function of the semi-supervised co-clustering ensemble is formulated according to normalized mutual information. Then, a kernel probabilistic model for semi-supervised co-clustering ensemble (KPMSCE) is presented and the inference of KPMSCE is illustrated in detail. Furthermore, the corresponding algorithm is designed. Moreover, different algorithms and the proposed algorithm are used for experiments on real datasets. The experimental results demonstrate that the proposed algorithm can significantly outperform the compared algorithms in terms of several indices.