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In 2010, we proposed the improved unsupervised possibilistic clustering algorithm (IUPC) that can be run as an unsupervised clustering and overcome the weakness of the unsupervised possibilistic clustering algorithm (UPC) that it tends to generate coincident clusters. IUPC inherits the merits of UPC. In the meanwhile, IUPC solves the coincident clusters problem of UPC by limiting the feasible regions...
In 2008, we proposed a clustering algorithm called improved kernel based fuzzy c-means clustering algorithm (IKFCM) to improve the performance of the original fuzzy c-means clustering algorithm. In this paper, we analyze the convergence of the IKFCM by means of Zangwill's convergence theorem. The result shows that arbitrary sequences generated by IKFCM always terminates at a local minimum or saddle...
In this paper, based on the analysis of the characteristics of magnetic resonance imaging (MRI), a novel fuzzy clustering algorithm for segmentation of brain MR images is presented. This new algorithm is developed by extending the conventional fuzzy clustering algorithm, which can compensate for not only the noise effects but also the intensity inhomogeneities of the MR images. The proposed technique...
A possibilistic clustering algorithm called unsupervised possibilistic clustering (UPC) was proposed in a previous paper. Although UPC is sound, the algorithm has the problem of generating coincident clusters. In this paper, we propose a new clustering model called improved unsupervised possibilistic clustering (IUPC) to overcome this weakness of UPC, and an efficient global optimization technique-differential...
In this paper, an unsupervised fuzzy technique for segmentation of brain magnetic resonance (MR) images is presented, which combines fuzzy clustering algorithm with maximum a posteriori (MAP) criterion. As fuzzy C-means (FCM) tends to balance the number of points in each cluster, cluster centers of smaller clusters are drawn to larger adjacent clusters. In order to overcome this problem occurred in...
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