The following information was submitted:
Transactions: WSEAS TRANSACTIONS ON COMPUTERS
Transactions ID Number: 89-790
Full Name: JENG-MING YIH
Position: Associate Professor
Age: ON
Sex: Male
Address: 140 Min-Sheng Rd., Taichung City 403, Taiwan
Country: TAIWAN
Tel: 886-4-22183511
Tel prefix: +886423723497
Fax: +886422183500
E-mail address: yih@mail.ntcu.edu.tw
Other E-mails: yih@mail.ntcu.edu.tw
Title of the Paper: Normalized Mahalanobis Clustering Algorithm Based on FCM
Authors as they appear in the Paper:
Email addresses of all the authors:
Number of paper pages: 10
Abstract: The popular FCM (fuzzy c-means algorithm) based on Euclidean distance function converges to a local minimum of the objective 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, Gustafson-Kessel clustering algorithm needs added constraint of fuzzy covariance matrix, Gath-Geva clustering algorithm can only be used for the data with multivariate Gaussian distribution. In GK-algorithm, modified Mahalanobis distance with preserved volume was used. However, the added fuzzy covariance matrices in their distance measure were not directly derived from the objective function. In this paper, an improved Normalized Mahalanobis Clustering Algorithm Based on FCM by taking a new threshold value and a new convergent process is proposed. The experimental results of real data sets show that our proposed new algorithm has !
the best performance. Not only replacing the common covariance matrix with the correlation matrix in the objective function in the Normalized Mahalanobis Clustering Algorithm Based on FCM, but also replacing the threshold .
Keywords: FCM, Clustering Algorithm, GK-algorithm; GG-algorithm; Normalized Mahalanobis Distances
EXTENSION of the file: .pdf
Special (Invited) Session: Unsupervised Clustering Algorithm Based on Normalized Mahalanobis Distances
Organizer of the Session: 637-330
How Did you learn about congress: lyh@mail.ntcu.edu.tw
IP ADDRESS: 210.240.187.32
