A Data Mining Algorithm Based on Improved K-Means Clustering

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This paper purposes a K-means clustering algorithm based on improved filtering process. Thealgorithm improves the filtering process,The two minimum sample points are reasonable initial clustering centers. It makes the probability summary of data in a cluster as large as possible, and the probability summary of data in different clusters as small as possible. Experimental results show that the proposed algorithm can select the proper initial clustering center, and it is more compact and robust than thetraditional K-means clustering algorithm.

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2028-2031

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March 2014

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© 2014 Trans Tech Publications Ltd. All Rights Reserved

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[1] Anil K J. Data clustering: 50 years beyond K -Means [J]. Pattern Recognition Letters, 2010, 31(8), 651-666.

DOI: 10.1016/j.patrec.2009.09.011

Google Scholar

[2] Han Xiaogong hu for. K means clustering algorithm study [J]. Journal of dalian university of technology, 2009, 40 (3).

Google Scholar

[3] Ms wang, wang cheng, Feng Zhenyuan Ye Jinfeng. K means clustering algorithm study review [J]. Journal of electronic design engineering, 2012, 20 (7).

Google Scholar

[4] Hu wei. Improve the level of the k-means clustering algorithm [J]. Computer engineering and application, 2011, online version first.

Google Scholar

[5] J. Goldberger, S. Roweis, G. Hinton, and R. Salakhutdinov. Neighborhood component analysis [C], Advances in Neural Information Processing Systems (NIPS) 17, 2005, 513-520.

Google Scholar