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	An Initialization Method for Clustering High-Dimensional Data
	Chen, Luying ; Chen, Lifei ; Jiang, Qingshan ; Wang, Beizhan ; Shi, Liang ; Jiang QS(姜青山) ; Wang BZ(王备战) ; Shi L(史亮)
	2009
英文摘要	Conference Name:1st International Workshop on Database Technology and Applications. Conference Address: Wuhan, PEOPLES R CHINA. Time:APR 25-26, 2009.; In iterative refinement clustering algorithms, such as the various types of K-Means algorithms, the clustering results are very sensitive to the initial cluster centers. Conventional initialization methods tend to loss effectiveness due to the so-called "curse of dimensionality" when clustering high-dimensional data. In this paper, a local density based method is proposed to search for initial cluster centers on high-dimensional data. We define the probability density of a point as the amount of its highly similar neighborhoods with weight coefficient. Points with high density neighborhoods and low similarity are chosen as the initial cluster centers. Experimental results on real world datasets show the effectiveness of the proposed method.
语种	英语
出处	http://dx.doi.org/10.1109/DBTA.2009.87
出版者	IEEE COMPUTER SOC
内容类型	其他
源URL	[http://dspace.xmu.edu.cn/handle/2288/85715]
专题	软件学院－会议论文
推荐引用方式 GB/T 7714	Chen, Luying,Chen, Lifei,Jiang, Qingshan,et al. An Initialization Method for Clustering High-Dimensional Data. 2009-01-01.

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