A New Cluster Based Fuzzy Model Tree for Data Modeling

• Published in: Rough Sets, Fuzzy Sets, Data Mining and Granular Computing pp. 224 - 231
• Main Authors: Lee, Dae-Jong, Park, Sang-Young, Jung, Nahm-Chung, Chun, Myung-Geun
• Format: Book Chapter
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg
• Series: Lecture Notes in Computer Science
• Subjects: Data Modeling; Fuzzy Clustering; Model Tree
• ISBN: 9783540725299; 3540725296
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-540-72530-5_26

This paper proposes a fuzzy model tree, so-called c-fuzzy model tree, consisting of local linear models using fuzzy cluster for data modeling. 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 grades between centers and input attributes. The expansion of internal node is determined by comparing the error calculated at the parent node with the sum of ones at the child nodes. On the other hand, data prediction is performed with the linear model having the highest fuzzy membership value between input attributes and cluster centers at the leaf nodes. To show the effectiveness of the proposed method, we have applied this method to real world data set. We found that the proposed method showed better performance than the widely used methods, such as model tree and artificial neural networks.