A Spectral Clustering Algorithm Improved by P Systems

Using spectral clustering algorithm is diffcult to find the clusters in the cases that dataset has a large difference in density and its clustering effect depends on the selection of initial centers. To overcome the shortcomings, we propose a novel spectral clustering algorithm based on membrane computing framework, called MSC algorithm, whose idea is to use membrane clustering algorithm to realize the clustering component in spectral clustering. A tissue-like P system is used as its computing framework, where each object in cells denotes a set of cluster centers and velocity-location model is used as the evolution rules. Under the control of evolutioncommunication mechanism, the tissue-like P system can obtain a good clustering partition for each dataset. The proposed spectral clustering algorithm is evaluated on three artiffcial datasets and ten UCI datasets, and it is further compared with classical spectral clustering algorithms. The comparison results demonstrate the advantage of the proposed spectral clustering algorithm

Multiobjective fuzzy clustering approach based on tissue-like membrane systems

Fuzzy clustering problem is usually posed as an optimization problem. However, the existing researchhas shown that clustering technique that optimizes a single cluster validity index may not provide satisfactory results on different kinds of data sets. This paper proposes a multiobjective clustering frameworkfor fuzzy clustering, in which a tissue-like membrane system with a special cell structure is designed tointegrate a non-dominated sorting technique and a modified differential evolution mechanism. Based onthe multiobjective clustering framework, a fuzzy clustering approach is realized to optimize three cluster validity indices that can capture different characteristics. The proposed approach is evaluated on sixartificial and ten real-life data sets and is compared with several multiobjective and singleobjective techniques. The comparison results demonstrate the effectiveness and advantage of the proposed approachon clustering the data sets with different characteristics.National Natural Science Foundation of China No 61472328Chunhui Project Foundation of the Education Department of China No. Z2016148Chunhui Project Foundation of the Education Department of China No. Z2016143Research Foundation of the Education Department of Sichuan province No. 17TD003