A new initialization method for clustering categorical data

被引：0

作者：

Wu, Shu ^{[1
]}

Jiang, Qingshan ^{[1
]}

Huang, Joshua Zhexue ^{[2
]}

机构：

[1] Xiamen Univ, Sch Software, Xiamen 361005, Peoples R China

[2] Univ Hong Kong, E Business Technol Inst, Hong Kong, Hong Kong, Peoples R China

来源：

ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PROCEEDINGS | 2007年 / 4426卷

关键词：

data mining; cluster analysis; partitional clustering; categorical attribute; initialization method;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Performance of partitional clustering algorithms which converges to numerous local minima highly depends on initial cluster centers. This paper presents an initialization method which can be implemented to partitional clustering algorithms for categorical data sets with minimizing the numerical objective function. Experimental results show that the new initialization method is more efficient and stabler than the traditional one and can be implemented to large data sets for its linear time complexity.

引用

页码：972 / +

页数：3

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