Fuzzy clustering algorithm for fuzzy data based on α-cuts

被引：7

作者：

Effati, S. ^{[1
]}

Yazdi, H. Sadoghi ^{[1
]}

Sharahi, A. Jiryani ^{[2
]}

机构：

[1] Ferdowsi Univ Mashhad, Dept Appl Math, Mashhad, Iran

[2] Sabzevar Tarbiat Moallem Univ, Dept Math, Sabzevar, Iran

来源：

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS | 2013年 / 24卷 / 03期

关键词：

alpha-cut; fuzzy numbers; fuzzy c-means; fuzzy clustering; fuzzy data; dissimilarity measure; NUMBERS;

D O I：

10.3233/IFS-2012-0528

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 fuzzy clustering algorithm for fuzzy data based on alpha-cuts. A new suitable definition for distance between two arbitrary fuzzy numbers based on alpha-cuts is proposed. We then reformulate fuzzy c-means (FCM) with fuzzy data and fuzzy centers based on alpha-cuts. The effectiveness of the proposed clustering algorithm is tested for three fuzzy data sets and then it is compared with other methods; the fuzzy c-number (FCN) algorithm, Hathaway's FCM algorithm and the mixed-type variables FCM (MVFCM) algorithm.

引用

页码：511 / 519

页数：9

共 13 条

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