Research on K-Means clustering algorithm based on density and its application to customer segmentation

doi:10.3778/j.issn.1002-8331.2008.35.074

Computer Engineering and Applications ›› 2008, Vol. 44 ›› Issue (35): 246-248.DOI: 10.3778/j.issn.1002-8331.2008.35.074

• 工程与应用 • Previous Articles

Research on K-Means clustering algorithm based on density and its application to customer segmentation

XIANG Jian-chi^1,2,LIU Xiang-bin¹,ZI Wu-cheng²

1.Department of Computer Education，Hunan Normal University，Changsha 410081，China
2.School of Business，Central South University，Changsha 410083，China

Received:2008-08-21 Revised:2008-10-07 Online:2008-12-11 Published:2008-12-11
Contact: XIANG Jian-chi

基于密度的K-Means算法及在客户细分中的应用研究

向坚持^1,2,刘相滨¹,资武成²

1.湖南师范大学计算机教学部，长沙 410081
2.中南大学商学院，长沙 410083

通讯作者: 向坚持

Abstract

Abstract: The existing problems of K-Means clustering algorithm are carefully researched.An improved K-Means Algorithm based on Density（KMAD） is presented，with which K value of clustering number is located according to the clustering objects distribution density of regional space，and it uses centroids of high-density region as initial clustering center points.Theory analysis and experimental results demonstrate that the improved algorithm can get better clustering than traditional K-Means algorithm at clustering validity and stability，and applied it to customer segmentation.

Key words: K-Means algorithm, K-Means Algorithm based on Density（KMAD） algorithm, density, customer segmentation

摘要： 针对K-Means算法所存在的问题进行了深入研究，提出了基于密度的K-Means算法（KMAD算法）。该算法采用聚类对象区域空间的密度分布方法来确定聚类个数K的值，然后用高密度区域的质心作为K-Means算法的初始聚类中心。理论分析与实验结果表明了改进算法的有效性和稳定性，并将改进的算法应用于客户细分研究中。

关键词: K-Means算法, KMAD算法, 密度, 客户细分

XIANG Jian-chi^1,2,LIU Xiang-bin¹,ZI Wu-cheng². Research on K-Means clustering algorithm based on density and its application to customer segmentation[J]. Computer Engineering and Applications, 2008, 44(35): 246-248.

向坚持^1,2,刘相滨¹,资武成². 基于密度的K-Means算法及在客户细分中的应用研究[J]. 计算机工程与应用, 2008, 44(35): 246-248.

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Research on K-Means clustering algorithm based on density and its application to customer segmentation

基于密度的K-Means算法及在客户细分中的应用研究

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