Interval Type-2 Fuzzy Clustering Algorithm using the Combination of the Fuzzy and Possibilistic C-Mean Algorithms

被引：0
|
作者：
Rubio, E. [1 ]
Castillo, O. [1 ]
机构：
[1] Tijuana Inst Technol, Div Grad Studies & Res, Tijuana, Mexico
来源：
2014 IEEE CONFERENCE ON NORBERT WIENER IN THE 21ST CENTURY (21CW) | 2014年
关键词：
clustering algorithms; fuzzy logic; interval type-2 fuzzy logic; fuzzy partition matrix; TISSUE DIFFERENTIATION;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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.
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页数：6
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