An efficient approach for building customer profiles from business data
Expert Systems with Applications: An International Journal
Effective fuzzy c-means based kernel function in segmenting medical images
Computers in Biology and Medicine
Naïve bayes vs. support vector machine: resilience to missing data
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part II
Postsupervised hard c-means classifier
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
Effective fuzzy c-means clustering algorithms for data clustering problems
Expert Systems with Applications: An International Journal
Strong fuzzy c-means in medical image data analysis
Journal of Systems and Software
Two novel fuzzy clustering methods for solving data clustering problems
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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A novel membership function and a fuzzy clustering approach derived from a viewpoint of iteratively reweighted least square (IRLS) techniques resolve the problem of singularity in the regular fuzzy c-means (FCM) clustering. An FCM classifier using the membership function and Mahalanobis distances makes class memberships of outliers less clear-cut, which thus resolve the problem of classification based on normal populations or normal mixtures. The ways of handling singular covariance matrices and missing values are also furnished, which improve the generalization capability of the classifier. Computational experiments show high classification performance on several well-known benchmark data sets.