scholarly journals Robust Approaches for Fuzzy Clusterwise Regression Based on Trimming and Constraints

2018 ◽  
pp. 155-164
Author(s):  
Luis Angel García-Escudero ◽  
Alfonso Gordaliza ◽  
Francesca Greselin ◽  
Agustín Mayo-Iscar
Keyword(s):  
Clusterwise Regression

Cautionary Remarks on the Use of Clusterwise Regression

2008 ◽  
Vol 43 (1) ◽  
pp. 29-49 ◽  
Author(s):  
Michael J. Brusco ◽  
J. Dennis Cradit ◽  
Douglas Steinley ◽  
Gavin L. Fox
Keyword(s):  
Clusterwise Regression

A class of fuzzy clusterwise regression models

2010 ◽  
Vol 180 (24) ◽  
pp. 4737-4762 ◽  
Author(s):  
Pierpaolo D’Urso ◽  
Riccardo Massari ◽  
Adriana Santoro
Keyword(s):  
Regression Models ◽  
Clusterwise Regression

Modifying Cluster-Based Segments to Enhance Agreement with an Exogenous Response Variable

1996 ◽  
Vol 33 (3) ◽  
pp. 351-363 ◽  
Author(s):  
Abba M. Krieger ◽  
Paul E. Green
Keyword(s):  
Exogenous Variable ◽  
Sums Of Squares ◽  
Exogenous Variables ◽  
Market Segments ◽  
Response Variable ◽  
Clusterwise Regression ◽  
Percentage Decrease

K-means clustering programs are frequently used to group buyers into market segments. Segment members exhibit similar background profiles, based on such characteristics as psychographics, benefits seeking, conjoint-based partworths, and so on. In addition, researchers also may use data on one or more exogenous variables for the same respondents. The authors describe and apply an algorithm for systematically modifying the original K-means segmentation to enhance prediction of an exogenous variable (either continuous or categorical). The modification is designed to respect a user-specified constraint on the permissible percentage decrease in the initial partition's ratio of among-groups sums of squares to total-groups sums of squares. The authors compare this approach with models drawn from the highly popular clusterwise regression literature.

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