K-Medoids-Based Consensus Clustering Based on Cell-Like P Systems with Promoters and Inhibitors

Consensus clustering is a class of robust clustering algorithms, which obtain the finally clustering results based on multiple existing basic partitionings. In this study, we introduce the K-medoids algorithm and the cell-like P systems with promoters and inhibiters (a class of parallel and distributed computing models) to the consensus clustering, and propose the K-medoids-based consensus clustering based on the cell-like P system with promoters and inhibiters. Through the experiment, the proposed consensus clustering algorithm can obtain high quality clustering results in a short time. This study improves the result in TKDE, 2015, 2, 155–169.