Lattice-based biclustering using Partition Pattern Structures

Victor Codocedo 1 Amedeo Napoli 1
1 ORPAILLEUR - Knowledge representation, reasonning
Inria Nancy - Grand Est, LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
Abstract : In this work we present a novel technique for exhaustive bicluster enumeration using formal concept anal-ysis (FCA). Particularly, we use pattern structures (an ex-tension of FCA dealing with complex data) to mine similar row/column biclusters, a specialization of biclustering when attribute values have coherent variations. We show how bi-clustering can benefit from the FCA framework through its ro-bust theoretical description and efficient algorithms. Finally, we evaluate our bicluster mining approach w.r.t. a standard biclustering technique showing very good results in terms of bicluster quality and performance.
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Victor Codocedo, Amedeo Napoli. Lattice-based biclustering using Partition Pattern Structures. ECAI 2014 - 21st European Conference on Artificial Intelligence, 18-22 August 2014, Prague, Czech Republic - Including Prestigious Applications of Intelligent Systems (PAIS) 2014, Aug 2014, Prague, Czech Republic. ⟨10.3233/978-1-61499-419-0-213⟩. ⟨hal-01095865⟩

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