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Title: Block-relaxation approaches for fitting the INDCLUS model
Authors: Wilderjans, Tom ×
Depril, Dirk
Van Mechelen, Iven #
Issue Date: 2012
Publisher: Published for the Classification Society of North America by Springer-Verlag New York
Series Title: Journal of Classification vol:29 pages:277-296
Abstract: A well-known clustering model to represent I x I x J data blocks, the J frontal slices of which consist of I x I object by object similarity matrices, is the INDCLUS model. This model implies a grouping of the I objects into a prespecified number of overlapping clusters, with each cluster having a slice-specific positive weight. An INDCLUS model is fitted to a given data set by means of minimizing a least squares loss function. The minimization of this loss function has appeared to be a difficult problem for which several algorithmic strategies have been proposed. At present, the best available option seems to be the SYMPRES algorithm, which minimizes the loss function by means of a block-relaxation algorithm. Yet, SYMPRES is conjectured to suffer from a severe local optima problem. As a way out, based on theoretical results with respect to optimally designing block-relaxation algorithms, five alternative block-relaxation algorithms are proposed. In a simulation study it appears that the alternative algorithms with overlapping parameter subsets perform best and clearly outperform SYMPRES in terms of optimization performance and cluster recovery.
ISSN: 0176-4268
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Methodology of Educational Sciences
Quantitative Psychology and Individual Differences
× corresponding author
# (joint) last author

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