Paper Info
Title
A structured view on pattern mining-based biclustering
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. HighlightsPattern mining (PM) searches enable flexible, exhaustive and efficient biclusteringIntegration of existing dispersed PM-inspired contributions for biclustering.Principles for guided design and evaluation of new PM-based biclustering approachesPM-based biclustering solutions have parameterizable coherency and quality.
Year
DOI
Venue
2015
10.1016/j.patcog.2015.06.018
Pattern Recognition
Keywords
Field
DocType
Biclustering,Pattern mining
Row,Data mining,Matrix (mathematics),Combinatorial optimization,Robustness (computer science),Coherence (physics),Artificial intelligence,Biclustering,Mathematics,Machine learning
Journal
Volume
Issue
ISSN
48
12
0031-3203
Citations 
PageRank 
References 
22
0.90
97
Authors
3
Name
Order
Citations
PageRank
Rui Henriques114312.35
Cláudia Antunes216116.57
Sara C. Madeira3124266.91