Modeling of Self-Constructed Clustering and Performance Evaluation 


Vol. 30,  No. 6, pp. 490-496, Jun.  2005


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  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

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  Cite this article

[IEEE Style]

J. w. Ryu, S. S. Kim, C. k. Song, S. S. Kim, "Modeling of Self-Constructed Clustering and Performance Evaluation," The Journal of Korean Institute of Communications and Information Sciences, vol. 30, no. 6, pp. 490-496, 2005. DOI: .

[ACM Style]

Jeong woong Ryu, Sung Suk Kim, Chang kyu Song, and Sung Soo Kim. 2005. Modeling of Self-Constructed Clustering and Performance Evaluation. The Journal of Korean Institute of Communications and Information Sciences, 30, 6, (2005), 490-496. DOI: .

[KICS Style]

Jeong woong Ryu, Sung Suk Kim, Chang kyu Song, Sung Soo Kim, "Modeling of Self-Constructed Clustering and Performance Evaluation," The Journal of Korean Institute of Communications and Information Sciences, vol. 30, no. 6, pp. 490-496, 6. 2005.

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pISSN : 1226 - 4717
eISSN : 2287 - 3880