Biclustering Numerical Data in Formal Concept Analysis

DOI10.1007/978-3-642-20514-9_12zbMath1326.68286OpenAlexW2115393608MaRDI QIDQ3003415

Mehdi Kaytoue, Amedeo Napoli, Sergei O. Kuznetsov

Publication date: 27 May 2011

Published in: Formal Concept Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-642-20514-9_12

zbMATH Keywords

formal concept analysisbiclusteringnumerical datapattern structuresconceptual scaling

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Knowledge representation (68T30)

Related Items (4)

Mint: MDL-based approach for mining interesting numerical pattern sets ⋮ Graph-FCA meets pattern structures ⋮ Biclustering meets triadic concept analysis ⋮ Enumerating all maximal biclusters in numerical datasets

Uses Software

• FCALGS

Cites Work

• Two FCA-Based Methods for Mining Gene Expression Data
• Comparing performance of algorithms for generating concept lattices
• Computing iceberg concept lattices with Titanic
• Similarity and Fuzzy Tolerance Spaces
• Actionability and Formal Concepts: A Data Mining Perspective
• AI*IA 2005: Advances in Artificial Intelligence
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