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Modeling of Self-Constructed Clustering and Performance Evaluation  

Ryu Jeong woong (충북대학교 전기전자컴퓨터공학부)
Kim Sung Suk (충북대학교 전기전자컴퓨터공학부)
Song Chang kyu (충북대학교 전기전자컴퓨터공학부)
Kim Sung Soo (충북대학교 전기전자컴퓨터공학부)
Publication Information
The Journal of Korean Institute of Communications and Information Sciences / v.30, no.6C, 2005 , pp. 490-496 More about this Journal
Abstract
In this paper, we propose a self-constructed clustering algorithm based on inference information of the fuzzy model. This method makes it possible to automatically detect and optimize the number of cluster and parameters by using input-output data. The propose method improves the performance of clustering by extended supervised learning technique. This technique uses the output information as well as input characteristics. For effect the similarity measure in clustering, we use the TSK fuzzy model to sent the information of output. In the conceptually, we design a learning method that use to feedback the information of output to the clustering since proposed algorithm perform to separate each classes in input data space. We show effectiveness of proposed method using simulation than previous ones
Keywords
MLE; Fuzzy Clustering; Subtractive Clustering; TSK Fuzzy Model; Supervised Learning;
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