Fuzzy clustering is well known as a robust and efficient way to reduce computation cost to obtain the better results. In the literature, many robust fuzzy clustering models have been presented such as Fuzzy C-Mean (FCM) and Possibilistic C-Mean (PCM), where these methods are Type-I Fuzzy clustering. Type-II Fuzzy sets, on the other hand, can provide better performance than Type-I Fuzzy sets, es...

Intuitionistic fuzzy sets are generalized fuzzy sets whose elements are characterized by a membership, as well as a non-membership value. The membership value indicates the degree of belongingness, whereas the nonmembership value indicates the degree of non-belongingness of an element to that set. The utility of intuitionistic fuzzy sets theory in computer vision is increasingly becoming appare...

We introduce fuzzy closure systems and fuzzy closure operators as extensions of closure systems and closure operators. We study relationships between fuzzy closure systems and fuzzy closure spaces. In particular, two families F (S) and F (C) of fuzzy closure systems and fuzzy closure operators on X are complete lattice isomorphic.

We investigate some new properties of intuitionistic fuzzy competition graphs. We present the construction of intuitionistic fuzzy competition graph. We study new type of intuitionistic fuzzy graphs by considering intuitionistic fuzzy open neighbourhood and intuitionistic fuzzy closed neighbourhood of the vertices. We also present an application of intuitionistic fuzzy competition graphs. c ©20...

Kernelized Fuzzy C-Means clustering technique is an attempt to improve the performance of the conventional Fuzzy C-Means clustering technique. Recently this technique where a kernel-induced distance function is used as a similarity measure instead of a Euclidean distance which is used in the conventional Fuzzy C-Means clustering technique, has earned popularity among research community. Like th...

In 2001 we introduced the notions of possibilistic mean value and variance of fuzzy numbers. In this paper we list some works that use these notions. We shall mention some application areas as well. 1 Possibilistic mean value, variance, covariance and correlation A fuzzy number A is a fuzzy set R with a normal, fuzzy convex and continuous membership function of bounded support. The family of fu...

Alaca, C. , Turkoglu, D. and Yildiz, C. , Fixed points in intuitionistic fuzzy metric spaces,Chaos, Solitons and Fractals, 29(2006), 10731078. Atanassov, K. , Intuitionistic fuzzy sets, Fuzzy sets and systems 20(1986) 87-96. Dubois, D. and Prade, H. , Fuzzy Sets: Theory and Applications to Policy Analysis and Information Systems Plenum Press, New York, 1980. George, A. and Veeramani, P. , On so...

Much research has shown that fuzzy c-means clustering is a powerful tool for partitioning samples into different categories. However, the cost function of the classical fuzzy c-means (FCM) is defined by the distances from data to the cluster centers with their fuzzy memberships. In this study, a new fuzzy clustering algorithm, namely the fuzzy weighted c-means (FWCM), is proposed. In this propo...

In data mining, the conventional clustering algorithms have difficulties in handling the challenges posed by the collection of natural data which is often vague and uncertain. Fuzzy clustering methods have the potential to manage such situations efficiently. This paper introduces the limitations of conventional clustering methods through k-means and fuzzy c-means clustering and demonstrates the...