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Proceedings Paper

Performance research of Gaussian function weighted fuzzy C-means algorithm
Author(s): Xiaofang Liu; Xiaowen Li
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Paper Abstract

Fuzzy C-Means (FCM) algorithm is a fuzzy pattern recognition method. Clustering precision of the algorithm is affected by its equal partition trend for data set of large discrepancy of each class samples number, and the optimal clustering result of the algorithm mightn't be a right partition in this case. In order to overcome this disadvantage, a Gaussian function Weighted Fuzzy C-Means (WFCM) algorithm is proposed, which the weighted function is produced by a Gaussian function calculating dot density of each sample. To certain extent, the WFCM algorithm has not only overcome the limitation of equal partition trend in fuzzy Cmeans algorithm, but also been favorable convergence and stability. The calculation of the weighted function and the choice of sample dot density range restriction value for the algorithm are both objective. When partially supervised information obtained from a few labeled samples is introduced to the WFCM algorithm, the classification performance of the WFCM algorithm is further enhanced and the convergent speed of objective function is further accelerated.

Paper Details

Date Published: 15 November 2007
PDF: 7 pages
Proc. SPIE 6788, MIPPR 2007: Pattern Recognition and Computer Vision, 67881Q (15 November 2007); doi: 10.1117/12.750060
Show Author Affiliations
Xiaofang Liu, Univ. of Electronic Science and Technology of China (China)
Sichuan Univ. of Science and Engineering (China)
Xiaowen Li, Univ. of Electronic Science and Technology of China (China)

Published in SPIE Proceedings Vol. 6788:
MIPPR 2007: Pattern Recognition and Computer Vision
S. J. Maybank; Mingyue Ding; F. Wahl; Yaoting Zhu, Editor(s)

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