An unsupervised learning algorithm for membrane computing

作者：Peng Hong^*; Wang Jun; Perez Jimenez Mario J; Riscos Nunez Agustin

来源：Information Sciences, 2015, 304: 80-91.

DOI：10.1016/j.ins.2015.01.019

• Membrane computing
• P system
• Evolution-communication P system
• Unsupervised learning
• Data clustering
• Fuzzy clustering

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.

• 出版日期2015-5-20
• 单位西华大学