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

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  • Tom Wilderjans
  • Dirk Depril
  • Iven Van Mechelen

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

Suggested Citation

  • Tom Wilderjans & Dirk Depril & Iven Van Mechelen, 2013. "Additive Biclustering: A Comparison of One New and Two Existing ALS Algorithms," Journal of Classification, Springer;The Classification Society, vol. 30(1), pages 56-74, April.
    • Handle: RePEc:spr:jclass:v:30:y:2013:i:1:p:56-74
    DOI: 10.1007/s00357-013-9120-0
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    References listed on IDEAS

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    Cited by:

    1. Doove, Lisa L. & Wilderjans, Tom F. & Calcagnì, Antonio & Van Mechelen, Iven, 2017. "Deriving optimal data-analytic regimes from benchmarking studies," Computational Statistics & Data Analysis, Elsevier, vol. 107(C), pages 81-91.
    2. Julian Rossbroich & Jeffrey Durieux & Tom F. Wilderjans, 2022. "Model Selection Strategies for Determining the Optimal Number of Overlapping Clusters in Additive Overlapping Partitional Clustering," Journal of Classification, Springer;The Classification Society, vol. 39(2), pages 264-301, July.
    3. Michael Brusco & Patrick Doreian, 2015. "An Exact Algorithm for the Two-Mode KL-Means Partitioning Problem," Journal of Classification, Springer;The Classification Society, vol. 32(3), pages 481-515, October.

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