Abstract
K-medoids algorithm is a classical algorithm used for clustering, it is developed from K-means algorithm and it is more robust compared to K-means algorithm for noises and outliers. But it takes more time to achieve a better result. In this paper, we proposed a hybrid algorithm to overcome the drawbacks. We combined K-means algorithm and an improved K-medoids algorithm together. Firstly, to have an elementary clustering results, we run K-means algorithm for the data set. Then, the improved K-medoids algorithm are used to optimize the results to make it more robust. Furthermore, we designed a Tissue-like P system for the proposed approach, the Tissue-like P system operates in a parallel way thus can improve time efficiency greatly. We tested the efficiency and effectiveness of our approach on some data sets of the well-known UCI benchmark and compared the approach with the K-means and the K-medoids algorithm.
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Acknowledgments
This work is supported by the Natural Science Foundation of China (nos. 61472231, 61502283, 61170038), 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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Wang, S., Xiang, L., Liu, X. (2018). A Hybrid Approach Optimized by Tissue-Like P System for Clustering. In: Peng, Y., Yu, K., Lu, J., Jiang, X. (eds) Intelligence Science and Big Data Engineering. IScIDE 2018. Lecture Notes in Computer Science(), vol 11266. Springer, Cham. https://doi.org/10.1007/978-3-030-02698-1_37
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DOI: https://doi.org/10.1007/978-3-030-02698-1_37
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