Block-Relaxation Approaches for Fitting the INDCLUS Model

Tom Wilderjans (), Dirk Depril and Iven Mechelen

Journal of Classification, 2012, vol. 29, issue 3, 277-296

Abstract: A well-known clustering model to represent I × I × J data blocks, the J frontal slices of which consist of I × 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. Copyright Springer Science+Business Media, LLC 2012

Keywords: INDCLUS; ADCLUS; Alternating least squares; Block-Relaxation algorithms; Additive clustering; Overlapping clusters; Proximity data; Three-Way data (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s00357-012-9113-4

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