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http://dx.doi.org/10.5391/JKIIS.2006.16.5.608

Data Modeling using Cluster Based Fuzzy Model Tree  

Lee, Dae-Jong (충북대학교 BK21 충북정보기술사업단)
Park, Jin-Il (충북대학교 전기전자컴퓨터공학부)
Park, Sang-Young (한국 수자원공사 수자원연구원)
Jung, Nahm-Chung (한국 수자원공사 수자원연구원)
Chun, Meung-Geun (충북대학교 전기전자컴퓨터공학부)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.16, no.5, 2006 , pp. 608-615 More about this Journal
Abstract
This paper proposes a fuzzy model tree consisting of local linear models using fuzzy cluster for data modeling. First, cluster centers are calculated by fuzzy clustering method using all input and output attributes. And then, linear models are constructed at internal nodes with fuzzy membership values between centers and input attributes. The expansion of internal node is determined by comparing errors calculated in parent node with ones in child node, respectively. As a final step, data prediction is performed with a linear model having the highest fuzzy membership value between input attributes and cluster centers in leaf nodes. To show the effectiveness of the proposed method, we have applied our method to various dataset. Under various experiments, our proposed method shows better performance than conventional model tree and artificial neural networks.
Keywords
Model tree; Fuzzy clustering; data prediction; M5P;
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