L1-norm based fuzzy clustering
Fuzzy Sets and Systems
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
A note on the Gustafson-Kessel and adaptive fuzzy clustering algorithms
IEEE Transactions on Fuzzy Systems
Fuzzy order statistics and their application to fuzzy clustering
IEEE Transactions on Fuzzy Systems
Generalized fuzzy c-means clustering strategies using Lp norm distances
IEEE Transactions on Fuzzy Systems
Optimality test for generalized FCM and its application to parameter selection
IEEE Transactions on Fuzzy Systems
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In fuzzy clustering, the fuzzy c-means (FCM) algorithm is the best known and most used method. Although FCM is a very useful method, it is sensitive to noise and outliers so that Wu and Yang (2002) proposed an alternative FCM (AFCM) algorithm. In this paper, we consider the AFCM algorithms with L1-norm and fuzzy covariance. These generalized AFCM algorithms can detect elliptical shapes of clusters and also robust to noise and outliers. Some numerical experiments are performed to assess the performance of the proposed algorithms. Numerical results clearly indicate the proposed algorithms to be superior to the existing methods.