ISSN:
1860-0980
Keywords:
additive clustering
;
nonhierarchical clustering
;
combinatorial optimization
;
three-way clustering
;
individual differences clustering
Source:
Springer Online Journal Archives 1860-2000
Topics:
Psychology
Notes:
Abstract We present a new model and associated algorithm, INDCLUS, that generalizes the Shepard-Arabie ADCLUS (ADditive CLUStering) model and the MAPCLUS algorithm, so as to represent in a clustering solution individual differences among subjects or other sources of data. Like MAPCLUS, the INDCLUS generalization utilizes an alternating least squares method combined with a mathematical programming optimization procedure based on a penalty function approach to impose discrete (0,1) constraints on parameters defining cluster membership. All subjects in an INDCLUS analysis are assumed to have a common set of clusters, which are differentially weighted by subjects in order to portray individual differences. As such, INDCLUS provides a (discrete) clustering counterpart to the Carroll-Chang INDSCAL model for (continuous) spatial representations. Finally, we consider possible generalizations of the INDCLUS model and algorithm.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1007/BF02294012
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