Variable Based Fuzzy Blocking Regression Model

Authors:
Mika Sato-Ilic
Affiliations:
Faculty of Systems and Information Engineering, University of Tsukuba, Tsukuba, Ibaraki 305-8573, Japan
Venue:
KES '07 Knowledge-Based Intelligent Information and Engineering Systems and the XVII Italian Workshop on Neural Networks on Proceedings of the 11th International Conference
Year:
2007

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Abstract

A fuzzy clustering based blocking regression model is proposed considering fuzzy intercepts and weights for each variable. The fuzzy intercepts and weights are obtained by using two fuzzy clustering results. One is a conventional fuzzy clustering over all variables and the other uses variable based fuzzy clustering. By involving the fuzzy clustering results, we can implement a regression model including nonlinear spatial data structures which are observed in a space consisting of all of the variables and a space consisting of each variable.