Interval type-2 fuzzy clustering algorithm using the combination of the fuzzy and possibilistic C-Mean algorithms
Elid Rubio, Oscar Castillo · 2014
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.