Fuzzy C-Means Algorithm Based on Common Mahalanobis Distances

ASIAIR > College of Computer Science > Department of Biomedical informatics > Journal Article > Item 310904400/8526

Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/8526

Title:	Fuzzy C-Means Algorithm Based on Common Mahalanobis Distances
Authors:	Liu, HC (Liu, Hsiang-Chuan);Yih, JM (Yih, Jeng-Ming);Lin, WC (Lin, Wen-Chih);Wu, DB (Wu, Der-Bang)
Contributors:	Department of Bioinformatics
Keywords:	Fuzzy C-Means algorithm;GK-algorithm;GG-algorithm;FCM-M algorithm;FCM-CM algorithm
Date:	2009
Issue Date:	2010-03-26 02:56:38 (UTC+0)
Publisher:	Asia University
Abstract:	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 (GK) clustering algorithm and Gath-Geva (GG) clustering algorithm were developed to detect non-spherical structural clusters. However, GK algorithm needs added constraint of fuzzy covariance matrix, GK algorithm can only be used for the data with multivariate Gaussian distribution. A Fuzzy C-Means algorithm based on Mahalanobis distance (FCM-M) was proposed by our previous work to improve those limitations of GG and GK algorithms, but it is not stable enough when some of its covariance matrices are not equal. In this paper, A improved Fuzzy C-Means algorithm based on a Common Mahalanobis distance (FCM-CM) is proposed The experimental results of three real data sets show that the performance of our proposed FCM-CM algorithm is better than those of the FCM, GG, GK and FCM-M algorithms.
Relation:	JOURNAL OF MULTIPLE-VALUED LOGIC AND SOFT COMPUTING 15 (5-6): 581-595
Appears in Collections:	[Department of Biomedical informatics ] Journal Article

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