MATEC Web Conf.
Volume 119, 2017The Fifth International Multi-Conference on Engineering and Technology Innovation 2016 (IMETI 2016)
|Number of page(s)||4|
|Published online||04 August 2017|
Clustering with fuzzy supervised algorithm
1 Department of Information Engineering, National Chung Cheng University, Chiayi 621, Taiwan
2 Department of Mathematics Education National Tai-Chung University of Education, Taichung City 403, Taiwan
3 Department of General Education, Min-Hwei Junior College of Health Care Management, Tainan 736, Taiwan
a Corresponding author : email@example.com
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. A Fuzzy C-Means algorithm based on Mahalanobis distance (FCM-M) was proposed 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, an improved Supervised 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 Supervised Clustering Algorithm.
© The Authors, published by EDP Sciences, 2017
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