Concept decompositions for large sparse text data using clustering
Machine Learning
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Information Retrieval
Modern Information Retrieval
Document clustering based on non-negative matrix factorization
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Information-theoretic co-clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
A heuristic-based fuzzy co-clustering algorithm for categorization of high-dimensional data
Fuzzy Sets and Systems
Comments on “A possibilistic approach to clustering”
IEEE Transactions on Fuzzy Systems
A Possibilistic Fuzzy c-Means Clustering Algorithm
IEEE Transactions on Fuzzy Systems
A novel possibilistic fuzzy leader clustering algorithm
International Journal of Hybrid Intelligent Systems - Rough and Fuzzy Methods for Data Mining
International Journal of Computational Intelligence Studies
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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 cmeans (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, we conduct further evaluation of PFCM for high-dimensional data and proposed a revised version of PFCM called Hyperspherical PFCM (HPFCM). Modifications have been made in the original PFCM objective function, so that cosine similarity measure could be incorporated in the approach. We apply both the original and revised approaches on six large benchmark data sets, and compare their performance with some of the traditional and recent clustering algorithms for automatic document categorization. Our analytical as well as experimental study show HPFCM is promising for handling complex high dimensional data sets and achieves more stable performance. On the other hand, the remaining problem of PFCM approach is also discussed.