A New Fuzzy Clustering Validity Index Based on Fuzzy Proximity Matrices

被引：3

作者：

Valente, Rafael Xavier ^{[1
]}

Braga, Antonio Padua ^{[1
]}

Pedrycz, Witold ^{[2
]}

机构：

[1] Univ Fed Minas Gerais, Grad Program Elect Engn, Av Antonio Carlos 6627, BR-31270901 Belo Horizonte, MG, Brazil

[2] Univ Alberta, Dept Elect & Comp Engn, Edmonton, AB T6G 2R3, Canada

来源：

2013 1ST BRICS COUNTRIES CONGRESS ON COMPUTATIONAL INTELLIGENCE AND 11TH BRAZILIAN CONGRESS ON COMPUTATIONAL INTELLIGENCE (BRICS-CCI & CBIC) | 2013年

关键词：

D O I：

10.1109/BRICS-CCI-CBIC.2013.87

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 a new validity index for fuzzy partitions generated by the fuzzy c-means algorithm. The proposed validity index is based on the calculation of factors from the proximity matrix generated from the membership matrix generated by a fuzzy clustering partition algorithm, such as FCM. The experimental results show that the proposed approach is consistent with other well-known metrics and with the dataset structure as observed from Proximity Matrices.

引用

页码：489 / 494

页数：6

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