Performance research of Gaussian function weighted fuzzy C-means algorithm

Xiaofang Liu; Xiaowen Li

doi:10.1117/12.750060

15 November 2007 Performance research of Gaussian function weighted fuzzy C-means algorithm

Xiaofang Liu, Xiaowen Li

Proceedings Volume 6788, MIPPR 2007: Pattern Recognition and Computer Vision; 67881Q (2007) https://doi.org/10.1117/12.750060
Event: International Symposium on Multispectral Image Processing and Pattern Recognition, 2007, Wuhan, China

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.

Citation Download Citation

Xiaofang Liu and Xiaowen Li "Performance research of Gaussian function weighted fuzzy C-means algorithm", Proc. SPIE 6788, MIPPR 2007: Pattern Recognition and Computer Vision, 67881Q (15 November 2007); https://doi.org/10.1117/12.750060

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