Paper Info
Title
Variable Based Fuzzy Blocking Regression Model
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.
Year
DOI
Venue
2007
10.1007/978-3-540-74827-4_66
KES (2)
Keywords
Field
DocType
fuzzy clustering,fuzzy blocking regression model,fuzzy intercept,nonlinear spatial data structure,uses variable,regression model,conventional fuzzy clustering,fuzzy clustering result,spatial analysis,nonlinear regression
k-medians clustering,Fuzzy clustering,CURE data clustering algorithm,Fuzzy classification,Correlation clustering,Pattern recognition,Regression analysis,Computer science,Fuzzy logic,Artificial intelligence,Cluster analysis
Conference
Volume
ISSN
Citations 
4693
0302-9743
0
PageRank 
References 
Authors
0.34
3
1
Name
Order
Citations
PageRank
Mika Sato-Ilic13216.09