Abstract
When samples number, classification and dimension of clustering are much more, traditional clustering algorithm usually leads to unharmonious character between clustering and transcendent knowledge. Therefore, a new clustering algorithm is proposed, which is parallel artificial immune clustering algorithm based on granular computing. Artificial immune system model has the characteristics, such as parallel, random searching and maintaining diversity, which can solve premature problem in latter evolution and converge to a global optimization solution faster. Besides, we unite it to dynamic granulation model and apply granulation description to clustering. In the process of granulation changing, we can choose appropriate granulation size by adjusting to ensure clustering efficiency and quality. Tests show that the algorithm is more effective and more reasonable when we handle clustering of some data with it.
Project supported by Special Foundation of Doctor’s Subject for Colleges and Universities (2006112005), National Natural Science Foundation of China (60374029), Visiting Scholar Foundation of Shanxi Province, P.R.C. (2004-18).
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Xie, K., Hao, X., Xie, J. (2007). Parallel Artificial Immune Clustering Algorithm Based on Granular Computing. In: An, A., Stefanowski, J., Ramanna, S., Butz, C.J., Pedrycz, W., Wang, G. (eds) Rough Sets, Fuzzy Sets, Data Mining and Granular Computing. RSFDGrC 2007. Lecture Notes in Computer Science(), vol 4482. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72530-5_24
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DOI: https://doi.org/10.1007/978-3-540-72530-5_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72529-9
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