Multi-objective genetic model for co-clustering ensemble

Abstract

Co-clustering ensemble establishes a consensus co-clustering over the data, and the ensemble process can be described as an optimization problem that can be solved by genetic algorithms. However, co-clustering ensemble methods based on genetic models are very few, in which fuzzy clustering and hard clustering is not combined. In this paper, a multi-objective genetic model for co-clustering ensemble (GMCCE) is proposed, and the corresponding objective function is designed. First, to process fuzzy samples and general samples more appropriately, bilateral fuzzy clustering and hard co-clustering are combined organically. Then, chromosomes are encoded as the membership of rows and columns, and after evolution process, the best chromosome is the consensus result. Finally, the proposed model is used to design a GMCCE algorithm. To evaluate the potential of GMCCE, extensive experiments are carried out, including comparison with base co-clustering algorithms and state-of-the-art algorithms. The results demonstrate that the GMCCE algorithm outperforms other algorithms.