An unsupervised learning algorithm for membrane computing

• Published in: Information sciences Vol. 304; pp. 80 - 91
• Main Authors: Peng, Hong, Wang, Jun, Pérez-Jiménez, Mario J., Riscos-Núñez, Agustín
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 20.05.2015
• Subjects: Algorithms; Clustering; Computation; Data clustering; Evolution; Evolution–communication P system; Fuzzy; Fuzzy clustering; Fuzzy logic; Fuzzy set theory; Membrane computing; Membranes; P system; Unsupervised learning; Membrane computing; Fuzzy clustering; Evolution–communication P system; P system; Unsupervised learning; Data clustering
• ISSN: 0020-0255; 1872-6291
• DOI: 10.1016/j.ins.2015.01.019

This paper focuses on the unsupervised learning problem within membrane computing, and proposes an innovative solution inspired by membrane computing techniques, the fuzzy membrane clustering algorithm. An evolution–communication P system with nested membrane structure is the core component of the algorithm. The feasible cluster centers are represented by means of objects, and three types of membranes are considered: evolution, local store, and global store. Based on the designed membrane structure and the inherent communication mechanism, a modified differential evolution mechanism is developed to evolve the objects in the system. Under the control of the evolution–communication mechanism of the P system, the proposed fuzzy clustering algorithm achieves good fuzzy partitioning for a data set. The proposed fuzzy clustering algorithm is compared to three recently-developed and two classical clustering algorithms for five artificial and five real-life data sets.