Additive Biclustering: A Comparison of One New and Two Existing ALS Algorithms

Tom Wilderjans (), Dirk Depril and Iven Van Mechelen

Journal of Classification, 2013, vol. 30, issue 1, 56-74

Abstract: The additive biclustering model for two-way two-mode object by variable data implies overlapping clusterings of both the objects and the variables together with a weight for each bicluster (i.e., a pair of an object and a variable cluster). In the data analysis, an additive biclustering model is fitted to given data by means of minimizing a least squares loss function. To this end, two alternating least squares algorithms (ALS) may be used: (1) PENCLUS, and (2) Baier’s ALS approach. However, both algorithms suffer from some inherent limitations, which may hamper their performance. As a way out, based on theoretical results regarding optimally designing ALS algorithms, in this paper a new ALS algorithm will be presented. In a simulation study this algorithm will be shown to outperform the existing ALS approaches. Copyright Springer Science+Business Media New York 2013

Keywords: Biclustering; Additive clustering; PENCLUS; ALS algorithms; Simulation study; Simultaneous overlapping clusterings; Co-clustering; Two-mode clustering; Two-mode data (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s00357-013-9120-0

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