文章基本信息

标题：An Optimized k-means Algorithm for Selecting Initial Clustering Centers
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作者：Jianhui Song ; Xuefei Li ; Yanju Liu 等
期刊名称：International Journal of Security and Its Applications
印刷版ISSN：1738-9976
出版年度：2015
卷号：9
期号：10
页码：177-186
DOI：10.14257/ijsia.2015.9.10.16
出版社：SERSC
摘要：Selecting the initial clustering centers randomly will cause an instability final result, and make it easy to fall into local minimum. To improve the shortcoming of the existing k- means clustering center selection algorithm, an optimized k-means algorithm for selecting initial clustering centers is proposed in this paper. When the number of the sample's maximum density parameter value is not unique, the distance between the plurality sample s with maximum density parameter values is calculated and compared with the average distance of the whole sample sets. The k optimized initial clustering centers are selected by combing the algorithm proposed in this paper with maximum distance means. The algorithm proposed in this paper is tested through the UCI dataset. The experimental results show the superiority of the proposed algorithm.
关键词：k-means; clustering center; density parameter; maximum distance