Fuzzy c-means clustering methods for symbolic interval data

被引：132
|
作者：
de Carvalho, Francisco de A. T. [1 ]
机构：
[1] Univ Fed Pernambuco, Ctr Informat, BR-50732970 Recife, PE, Brazil
来源：
PATTERN RECOGNITION LETTERS | 2007年 / 28卷 / 04期
关键词：
symbolic data analysis; fuzzy c-means clustering methods; symbolic interval data; squared euclidean distances; adaptive distances; fuzzy partition interpretation indices; fuzzy cluster interpretation indices;
D O I：
10.1016/j.patrec.2006.08.014
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper presents adaptive and non-adaptive fuzzy c-means clustering methods for partitioning symbolic interval data. The proposed methods furnish a fuzzy partition and prototype for each cluster by optimizing an adequacy criterion based on suitable squared Euclidean distances between vectors of intervals. Moreover, various cluster interpretation tools are introduced. Experiments with real and synthetic data sets show the usefulness of these fuzzy c-means clustering methods and the merit of the cluster interpretation tools. (c) 2006 Elsevier B.V. All rights reserved.
引用
收藏
页码：423 / 437
页数：15
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