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.
Original language | English |
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Pages (from-to) | 56-74 |
Number of pages | 19 |
Journal | Journal of Classification |
Volume | 30 |
Issue number | 1 |
DOIs | |
Publication status | Published - 11 Jan 2013 |
Externally published | Yes |
Keywords
- Additive clustering
- ALS algorithms
- Biclustering
- Co-clustering
- PENCLUS
- Simulation study
- Simultaneous overlapping clusterings
- Two-mode clustering
- Two-mode data