Journal
JOURNAL OF CLASSIFICATION
Volume 30, Issue 1, Pages 56-74Publisher
SPRINGER
DOI: 10.1007/s00357-013-9120-0
Keywords
Biclustering; Additive clustering; PENCLUS; ALS algorithms; Simulation study; Simultaneous overlapping clusterings; Co-clustering; Two-mode clustering; Two-mode data
Funding
- Research Fund of KU Leuven (PDM-kort project) [3H100377, GOA 2005/04]
- Belgian Science Policy [IAP P6/03]
- Fund of Scientific Research (FWO)-Flanders [G.0546.09]
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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.
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