Enumerating all maximal biclusters in numerical datasets

Rosana Veroneze, Arindam Banerjee, Fernando J. Von Zuben

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

Biclustering has proved to be a powerful data analysis technique due to its wide success in various application domains. However, the existing literature presents efficient solutions only for enumerating maximal biclusters with constant values, or heuristic-based approaches which cannot find all biclusters or even support the maximality of the obtained biclusters. Here, we present a general family of biclustering algorithms for enumerating all maximal biclusters with (i) constant values on rows, (ii) constant values on columns, or (iii) coherent values. Versions for perfect and for perturbed biclusters are provided. Our algorithms have four key properties (only the algorithm for perturbed biclusters with coherent values fails to exhibit the first property): they are (1) efficient (take polynomial time per pattern), (2) complete (find all maximal biclusters), (3) correct (all biclusters attend the user-defined measure of similarity), and (4) non-redundant (all the obtained biclusters are maximal and the same bicluster is not enumerated twice). They are based on a generalization of an efficient formal concept analysis algorithm called In-Close2. Experimental results point to the necessity of having efficient enumerative biclustering algorithms and provide a valuable insight into the scalability of our family of algorithms and its sensitivity to user-defined parameters.

Original languageEnglish (US)
Pages (from-to)288-309
Number of pages22
JournalInformation Sciences
Volume379
DOIs
StatePublished - Feb 10 2017

Bibliographical note

Funding Information:
R. Veroneze and F. J. Von Zuben would like to thank CAPES and CNPq for the financial support. A. Banerjee acknowledges support of NSF grants IIS-1447566 , IIS-1422557 , CCF-1451986 , CNS-1314560 , IIS-0953274 , IIS-1029711 , and by NASA grant NNX12AQ39A .

Publisher Copyright:
© 2016 Elsevier Inc.

Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

Keywords

  • Efficient enumeration
  • Maximal biclusters
  • Multiple types of biclusters
  • Numerical datasets
  • Perfect and perturbed biclusters

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