Showing papers by "Jian Zhou published in 2007"
TL;DR: Three types of fuzzy programming models – fuzzy expected cost minimization model, fuzzy a-cost minimizationmodel, and credibility maximization model – are proposed according to different decision criteria in order to model capacitated location–allocation problem with fuzzy demands.
122 citations
TL;DR: A general approach of fuzzy clustering is initiated from a new point of view, in which the memberships are estimated directly according to the data information using the fuzzy set theory, and the cluster centers are updated via a performance index.
Abstract: Fuzzy clustering is an approach using the fuzzy set theory as a tool for data grouping, which has advantages over traditional clustering in many applications. Many fuzzy clustering algorithms have been developed in the literature including fuzzy c-means and possibilistic clustering algorithms, which are all objective-function based methods. Different from the existing fuzzy clustering approaches, in this paper, a general approach of fuzzy clustering is initiated from a new point of view, in which the memberships are estimated directly according to the data information using the fuzzy set theory, and the cluster centers are updated via a performance index. This new method is then used to develop a generalized approach of possibilistic clustering to obtain an infinite family of generalized possibilistic clustering algorithms. We also point out that the existing possibilistic clustering algorithms are members of this family. Following that, some specific possibilistic clustering algorithms in the new family are demonstrated by real data experiments, and the results show that these new proposed algorithms are efficient for clustering and easy for computer implementation.
24 citations
01 Dec 2007
TL;DR: By introducing a novel dissimilarity index in the credibilistic clustering algorithm objective function, the proposed algorithm is not only capable of utilizing local contextual information to impose local spatial continuity, but also allows the suppression of noise and helps to resolve classification ambiguity.
Abstract: An image segmentation algorithm based on credibilistic clustering algorithm incorporating spatial continuity is presented in this paper. The probabilistic constraint that the memberships of a pixel across clusters must sum to 1 in fuzzy c-means algorithm is removed, and credibility measure is introduced into image segmentation for the first time. By introducing a novel dissimilarity index in the credibilistic clustering algorithm objective function, the proposed algorithm is not only capable of utilizing local contextual information to impose local spatial continuity, but also allows the suppression of noise and helps to resolve classification ambiguity. Some important issues of the proposed algorithm are investigated, and the computational experiments are given to show the good performance of the proposed algorithm.
15 citations