<bold>Clustering Categorical Data using Silhouette Coefficient as a Relocating Measure</bold>

被引：0
|
作者：
Aranganayagi, S. [1 ]
Thangavel, K. [2 ]
机构：
[1] JKK Nataraja Coll Arts & Sci, Komarapalayam 638183, Tamil Nadu, India
[2] Periyar Univ, Dept Comp Sci, Salem 636011, Tamil Nadu, India
来源：
ICCIMA 2007: INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND MULTIMEDIA APPLICATIONS, VOL III, PROCEEDINGS | 2007年
关键词：
data mining; clustering; categorical data; silhouette coefficient;
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：
Cluster analysis is an unsupervised learning method that constitutes a cornerstone of an intelligent data analysis process. Clustering categorical data is an important research area data mining. In this paper we propose a novel algorithm to cluster categorical data. Based on the minimum dissimilarity value objects are grouped into cluster. In the merging process, the objects are relocated using silhouette coefficient. Experimental results show that the proposed method is efficient.
引用
收藏
页码：13 / +
页数：3
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