An extensional fuzzy c-means clustering algorithm based on intuitionistic extension index

Author

Liu, Hsiang-chuan ; Yu, Yen-kuei ; Tsai, Hsien-chang ; Liu, Tung-sheng ; Jeng, Bai-cheng

Author_Institution

Dept. of Bioinf. & Med. Inf., Asia Univ., Taichung, Taiwan

Volume

1

fYear

2011

fDate

10-13 July 2011

Firstpage

199

Lastpage

203

Abstract

In this paper, a novel fuzzy c-means algorithm based on an intuitionistic extension index for any n-dimensional point set, namely the E-FCM algorithm, is being proposed. If the intuitionistic extension index is equal to 0, then the proposed new algorithm is just the traditional fuzzy c-means algorithm (FCM), in other words, the E-FCM algorithm is a generalization of the FCM algorithm. It is quite different from Xu and Wu´s intuitionistic fuzzy C-means clustering algorithm (IFCM algorithm), since the latter can only be used for intuitionistic fuzzy sets, but not for any n-dimensional point set. The experimental results of three benchmark data sets show that the proposed E-FCM algorithm outperforms the FCM algorithm.

Keywords

data analysis; fuzzy set theory; pattern clustering; E-FCM algorithm; IFCM algorithm; data analysis; data interpretation; extensional fuzzy c-means clustering algorithm; intuitionistic extension index; intuitionistic fuzzy c-means clustering algorithm; n-dimensional point set; Algorithm design and analysis; Clustering algorithms; Educational institutions; Equations; Fuzzy sets; Indexes; Machine learning; E-FCM algorithm; Fuzzy c-means; IFCM algorithm; Iintuitionistic fuzzy membership; Intuitionistic extension index;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics (ICMLC), 2011 International Conference on

Conference_Location

Guilin

ISSN

2160-133X

Print_ISBN

978-1-4577-0305-8

Type

conf

DOI

10.1109/ICMLC.2011.6016708

Filename

6016708