Fuzzy C-means algorithm based on standard mahalanobis distances
Hsiang-Chuan Liu, B.‐W. Jeng, Jeng-Ming Yih, Yen-Kuei Yu · 2009
Some of the well-known fuzzy clustering algorithms are based on Euclidean distance function, which can only be used to detect spherical structural clusters. Gustafson-Kessel clustering algorithm and Gath-Geva clustering algorithm were developed to detect non-spherical structural clusters. However, the former needs added constraint of fuzzy covariance matrix, the later can only be used for the data with multivariate Gaussian distribution. Two improved Fuzzy C-Means algorithm based on different Mahalanobis distance, called FCM-M and FCM-CM were proposed by our previous works, In this paper, A improved Fuzzy C-Means algorithm based on a standard Mahalanobis distance (FCM-SM) is proposed The experimental results of three real data sets show that our proposed new algorithm has the better performance.