Membrane Clustering: A Novel Clustering Algorithm under Membrane Computing

Abstract

Membrane computing (known as P systems) is a class of distributed parallel computing models, this paper presents a novel algorithm under membrane computing for solving the data clustering problem, called as membrane clustering algorithm. The clustering algorithm is based on a tissue-like P system with a loop structure of cells. The objects of the cells express the candidate cluster centers and are evolved by the evolution rules. Based on the loop membrane structure, the communication rules realize a local neighborhood topology, which helps the co-evolution of the objects and improves the diversity of objects in the system. The tissue-like P system can effectively search for the optimal clustering partition with the help of its parallel computing advantage. The proposed clustering algorithm is evaluated on four artificial data sets and six real-life data sets. Experimental results show that the proposed clustering algorithm is superior or competitive to classical k-means algorithm and several evolutionary clustering algorithms recently reported in the literature.