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A possibilistic fuzzy c-means (PFCM)[1] has been proposed for clustering unlabeled data. It is a hybridization of possibilistic c-means (PCM) and fuzzy c-means (FCM), therefore it has been shown that PFCM is able to solve the noise sensitivity issue in FCM, and at the same time it helps to avoid coincident clusters problem in PCM with some numerical examples in low-dimensional data sets. In this paper,...
In this paper, we develop a new soft model dual fuzzy-possibilistic coclustering (DFPC) for document categorization. The proposed model targets robustness to outliers and richer representations of coclusters. DFPC is inspired by an existing algorithm called possibilistic fuzzy C-means (PFCM) that hybridizes fuzzy and possibilistic clustering. It has been shown that PFCM can perform effectively for...
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