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Proceedings of the 2nd International Symposium on Information Processing (ISIP 2009)

Huangshan, China, August 21-23, 2009

Editors: Fei Yu, Jian Shu, and Guangxue Yue

AP Catalog Number: AP-PROC-CS-09CN002

ISBN: 978-952-5726-02-2 (Print), 978-952-5726-03-9 (CD-ROM)

Page(s): 422-427

Fuzzy C-Means Algorithm Based on Standard Mahalanobis Distances

Hsiang-Chuan Liu, Bai-Cheng Jeng, Jeng-Ming Yih, and Yen-Kuei Yu

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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.

Index Terms

GK-algorithm; GG-algorithm; FCM-M algorithm; FCM-CM algorithm; FCM-SM algorithm

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