A partitional clustering algorithm validated by a clustering tendency index based on graph theory

被引：21
|
作者：
Silva, HB [1 ]
Brito, P
da Costa, JP
机构：
[1] Polytech Sch Engn Porto, Dept Math, Oporto, Portugal
[2] Univ Porto, LIACC, Sch Econ, P-4100 Oporto, Portugal
[3] Univ Porto, Dept Appl Math, FC, P-4100 Oporto, Portugal
来源：
PATTERN RECOGNITION | 2006年 / 39卷 / 05期
关键词：
unsupervised learning; clustering algorithms; clustering validity;
D O I：
10.1016/j.patcog.2005.10.027
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Applying graph theory to clustering, we propose a partitional clustering method and a clustering tendency index. No initial assumptions about the data set are requested by the method. The number of clusters and the partition that best fits the data set, are selected according to the optimal clustering tendency index value. (c) 2005 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
引用
收藏
页码：776 / 788
页数：13
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