Interval type-2 fuzzy clustering algorithm using the combination of the fuzzy and possibilistic C-Mean algorithms

Author

Rubio, E. ; Castillo, Oscar

Author_Institution

Div. of Grad. Studies & Res., Tijuana Inst. of Technol., Tijuana, Mexico

fYear

2014

fDate

24-26 June 2014

Firstpage

1

Lastpage

6

Abstract

In this work the development of an interval type-2 fuzzy clustering algorithm, combining the Fuzzy C-Means (FCM) and Possibilistic C-Means (PCM) clustering algorithms is presented. The process of data clustering is carried out with a fuzzification exponent of m = 2. The development of the interval fuzzy clustering algorithm with a fixed fuzzification exponent (e.g. m = 2), instead of a fuzzification interval [m1, m2] consists of the combination of the FCM and PCM algorithms. This interval fuzzy clustering algorithm is possible because the computation of the used fuzzy partition matrices for each fuzzy clustering algorithm is different. This was proposed to overcome the disadvantages of not properly managing uncertainty in data clustering.

Keywords

fuzzy set theory; matrix algebra; pattern clustering; possibility theory; FCM; PCM; data clustering process; fixed fuzzification exponent; fuzzy c-mean algorithm; fuzzy partition matrices; interval type-2 fuzzy clustering algorithm; possibilistic c-mean algorithm; Clustering algorithms; Equations; Indexes; Mathematical model; Partitioning algorithms; Phase change materials; Proposals; clustering algorithms; fuzzy logic; fuzzy partition matrix; interval type-2 fuzzy logic;

fLanguage

English

Publisher

ieee

Conference_Titel

Norbert Wiener in the 21st Century (21CW), 2014 IEEE Conference on

Conference_Location

Boston, MA

Type

conf

DOI

10.1109/NORBERT.2014.6893879

Filename

6893879