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
收藏
页码：489 / 494
页数：6
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