基于特征点选择的聚类算法研究

J4 ›› 2009, Vol. 44 ›› Issue (9): 40-42.

基于特征点选择的聚类算法研究

朱国红石冰邢晓娜

1. 朱国红,石冰山东大学计算机科学与技术学院, 山东济南 250101;  2. 邢晓娜河南城建学院, 河南平顶山 467044

收稿日期:2009-05-20 出版日期:2009-09-16 发布日期:2009-11-05
作者简介:朱国红(1978-), 男,硕士研究生, 主要研究方向为数据挖掘. Email:zghong@foxmail.com

A clustering algorithm based on feature point selection

SHU Guo-Gong, DAN Bing, GENG Xiao-Na

1. School of Computer Science and Technology, Shandong University, Jinan 250101, Shandong, China;  2. Henan University of Urban Construction, Pingdingshan 467044, Henan, China

Received:2009-05-20 Online:2009-09-16 Published:2009-11-05

摘要/Abstract

摘要：

针对当前数据挖掘中对数值型数据聚类方法的不足,提出了基于特征点选择的聚类算法(clustering algorithm based on Feature Point Selection,CFPS)。CFPS算法可以克服需要输入聚类数量的缺陷, 算法本身可以找到簇的最佳数量,使聚类的精度和效率得到大大提高。实验结果表明该方法对数值型数据聚类方法具有借鉴意义和深入研究的价值。

关键词: 聚类; k均值; 数据挖掘

Abstract:

A clustering algorithm based on Feature Point Selection in Data Mining (abbreviated CFPS) is put forward in this paper. This method can overcome the disadvantage of a algorithm which requires the number of clusters for numerical incoming data. The CFPS algorithm finds the optimal number of clusters, andgreatly improves the precision and efficiency of clustering. The results of experiments prove that using the algorithm for the numerical data clustering method is feasible, which is valuable for further study in more depth.

Key words: clustering; k means; data mining

中图分类号:

TP311

朱国红石冰邢晓娜. 基于特征点选择的聚类算法研究[J]. J4, 2009, 44(9): 40-42.

SHU Guo-Gong, DAN Bing, GENG Xiao-Na. A clustering algorithm based on feature point selection[J]. J4, 2009, 44(9): 40-42.

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