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.National Natural Science Foundation of China No 61170030National Natural Science Foundation of China No 61472328Chunhui Project Foundation of the Education Department of China No. Z2012025Chunhui Project Foundation of the Education Department of China No. Z2012031Sichuan Key Technology Research and Development Program No. 2013GZX015
