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计算机系统应用英文版:2017,26(5):170-174
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K-means算法初始聚类中心选择的优化
(中国矿业大学 计算机科学与技术学院, 徐州 221116)
Optimization of Initial Clustering Centers Selection Method for K-Means Algorithm
(School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China)
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Received:August 01, 2016    Revised:September 23, 2016
中文摘要: 迄今为止,在数据挖掘领域,人们已经实现了多种聚类算法,其中使用最广泛的当属K-means聚类算法.然而,在数据挖掘中,K-means算法面临的一个主要问题就是初始中心点选择问题.本文提出了一种结合关系矩阵和度中心性(Degree Centrality)的分析方法,从而确定K-means算法初始的k个中心点.与传统方法相比,本文算法可得到更加优质的聚类结果.实验结果表明该算法的有效性和可行性.
中文关键词: 数据挖掘  度中心性  K-means算法  聚类
Abstract:So far, in the field of data mining, people have achieved a variety of algorithms of clustering. And the most widely used is K-means clustering algorithm. But the main problem of K-means is the initial center selection problem. In this paper, a method is proposed to determine the initial K centers of the K-means algorithm through the relationship matrix and the Degree Centrality. Compared with the traditional algorithm, the proposed algorithm can get the better clustering result. Experimental results have proved the validity and feasibility of this algorithm.
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郁启麟.K-means算法初始聚类中心选择的优化.计算机系统应用,2017,26(5):170-174
YU Qi-Lin.Optimization of Initial Clustering Centers Selection Method for K-Means Algorithm.COMPUTER SYSTEMS APPLICATIONS,2017,26(5):170-174