NDLI: Fuzzy C-mean algorithm based on “complete” Mahalanobis distances

Content Provider	IEEE Xplore Digital Library
Author	Hsiang-Chuan Liu Jeng-Ming Yih Der-Bang Wu Shin-Wu Liu
Copyright Year	2008
Description	Author affiliation: Dept. of Bioinf., Asia Univ., Taichung (Hsiang-Chuan Liu)
Abstract	The well known fuzzy partition clustering algorithms are most 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, but both of them based on semi-supervised Mahalanobis distance, these two algorithms fail to consider the relationships between cluster centers in the objective function, needing additional prior information. When some training cluster size is small than its dimensionality, it induces the singular problem of the inverse covariance matrix. It is an important issue. In our previous study, we developed a new unsupervised algorithm, FCM-M, to solve the singular problem of the inverse covariance matrix. But, the previous work only consider the local covariance matrix of each cluster. In this paper, an improved new unsupervised algorithm, ldquofuzzy c-mean based on complete Mahalanobis distance without any prior information (FCM-CM)rdquo, is proposed. The proposed new algorithm which is considered not only the local covariance matrix of each cluster but also the overall covariance matrix, which can get more information and higher accuracy by considering the overall covariance matrix. A real data set was applied to prove that the performance of the FCM-CM algorithm is better than those of the traditional FCM algorithm and our previous FCM-M. For choosing the better initial value of the same new algorithm, FCM-CM, the ratio method is still the best of all choosing methods by using in FCM-M and FCM algorithms in our previous works.
Starting Page	3569
Ending Page	3574
File Size	227984
Page Count	6
File Format	PDF
ISBN	9781424420957
DOI	10.1109/ICMLC.2008.4621023
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-07-12
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Clustering algorithms Covariance matrix Distance measurement Partitioning algorithms Artificial neural networks Machine learning algorithms Integrated circuits FCM-CM FCM Mahalanobis distances GK GG FCM-M
Content Type	Text
Resource Type	Article

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