Abstract
Cluster analysis, as a part of data analysis, appear to be extremely close fields of the modern computer science. A class of distributed parallel computing models: P systems, also known as Membrane systems, are widely used to solve the clustering problems. This paper presents an improved PSO-based clustering algorithm inspired by tissue-like P system, called as TPCA. The proposed clustering algorithm adopts the structure of tissue-like P system, which contains a loop of cells. An object in the cells represents a group of candidate cluster centers. Two kinds of rules are adopted in TPCA: communication rules and evolution rules. The communication rules build a local neighborhood topology in virtue of the loop structure of cells, which promotes the co-evolution of the objects and increases the diversity of objects in the system. Moreover, different PSO-based evolution rules are used to evolve common objects and poor objects respectively, which is beneficial to accelerate convergence of population. Experimental results on three synthetic data sets and six real-life data sets show that the proposed TPCA achieves a more ideal division compared to several evolutionary clustering algorithms recently reported, such as FERPSO, GA, DE, PSO and classical K-means algorithm.
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Acknowledgments
Research is supported by National Natural Science Foundation of China (61472231,61502283,61640201), Ministry of Education of Humanities and Social Science Research Project, China (12YJA630152), Social Science Fund Project of Shandong Province, China (16BGLJ06, 11CGLJ22).
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Gao, T., Liu, X., Wang, L. (2018). An Improved PSO-Based Clustering Algorithm Inspired by Tissue-Like P System. In: Tan, Y., Shi, Y., Tang, Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science(), vol 10943. Springer, Cham. https://doi.org/10.1007/978-3-319-93803-5_31
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