Fuzzy K-means clustering algorithms for interval-valued data based on adaptive quadratic distances

被引：83

作者：

de Carvalho, Francisco de A. T. ^{[1
]}

Tenorio, Camilo P. ^{[1
]}

机构：

[1] Univ Fed Pernambuco, Ctr Informat, BR-50740540 Recife, PE, Brazil

来源：

FUZZY SETS AND SYSTEMS | 2010年 / 161卷 / 23期

关键词：

Fuzzy statistics and data analysis; Symbolic data analysis; Fuzzy clustering; Interval-valued data; Adaptive quadratic distances; Fuzzy cluster interpretation indexes;

D O I：

10.1016/j.fss.2010.08.003

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

This paper presents partitioning fuzzy K-means clustering models for interval-valued data based on suitable adaptive quadratic distances. These models furnish a fuzzy partition and a prototype for each cluster by optimizing an adequacy criterion that measures the fit between the fuzzy clusters and their representatives. These adaptive quadratic distances change at each algorithm iteration and can be either the same for all clusters or different from one cluster to another. Moreover, additional interpretation tools for individual fuzzy clusters of interval-valued data, suitable to these fuzzy clustering models, are also presented. Experiments with some interval-valued data sets demonstrate the usefulness of these fuzzy clustering models and the merit of the individual fuzzy cluster interpretation tools. (C) 2010 Elsevier B.V. All rights reserved.

引用

页码：2978 / 2999

页数：22

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