Article In: cienciavitae, dblp, scopus, orcid
A structured view on pattern mining-based biclustering
Pattern Recognition
— 2015 — Pattern Recognit.
Key information
Authors:
Published in
December 1, 2015
Abstract
Mining matrices to find relevant biclusters, subsets of rows exhibiting a coherent pattern over a subset of columns, is a critical task for a wide-set of biomedical and social applications. Since biclustering is a challenging combinatorial optimization task, existing approaches place restrictions on the allowed structure, coherence and quality of biclusters. Biclustering approaches relying on pattern mining (PM) allow an exhaustive yet efficient space exploration together with the possibility to discover flexible structures of biclusters with parameterizable coherency and noise-tolerance. Still, state-of-the-art contributions are dispersed and the potential of their integration remains unclear. This work proposes a structured and integrated view of the contributions of state-of-the-art PM-based biclustering approaches, makes available a set of principles for a guided definition of new PM-based biclustering approaches, and discusses their relevance for applications in pattern recognition. Empirical evidence shows that these principles guarantee the robustness, efficiency and flexibility of PM-based biclustering.
Publication details
Authors in the community:
Rui Miguel Carrasqueiro Henriques
ist156846
Claudia Martins Antunes
ist14046
Sara Alexandra Cordeiro Madeira
ist46399
Publication version
P - Proof
Publisher
Pattern Recognit.
Link to the publisher's version
https://www.sciencedirect.com/science/article/abs/pii/S003132031500240X?via%3Dihub
Title of the publication container
Pattern Recognition
First page or article number
3941
Last page
3958
Volume
48
Issue
12
ISSN
0031-3203
Fields of Science and Technology (FOS)
computer-and-information-sciences - Computer and information sciences
Publication language (ISO code)
eng - English
Alternative identifier (URI)
http://www.scopus.com/inward/record.url?eid=2-s2.0-84941418114&partnerID=MN8TOARS
Rights type:
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