On the impact of dissimilarity measure in k-modes clustering algorithm

被引：137
|
作者：
Ng, Michael K. [1 ]
Li, Mark Junjie
Huang, Joshua Zhexue
He, Zengyou
机构：
[1] Hong Kong Baptist Univ, Dept Math, Hong Kong, Hong Kong, Peoples R China
[2] Univ Hong Kong, E Business Technol Inst, Hong Kong, Hong Kong, Peoples R China
[3] Harbin Inst Technol, Dept Comp Sci & Engn, Harbin 150001, Peoples R China
来源：
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE | 2007年 / 29卷 / 03期
基金：
中国国家自然科学基金;
关键词：
data mining; clustering; k-modes algorithm; categorical data;
D O I：
10.1109/TPAMI.2007.53
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This correspondence describes extensions to the k-modes algorithm for clustering categorical data. By modifying a simple matching dissimilarity measure for categorical objects, a heuristic approach was developed in [4], [12] which allows the use of the k- modes paradigm to obtain a cluster with strong intrasimilarity and to efficiently cluster large categorical data sets. The main aim of this paper is to rigorously derive the updating formula of the k- modes clustering algorithm with the new dissimilarity measure and the convergence of the algorithm under the optimization framework.
引用
收藏
页码：503 / 507
页数：5
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