Boolean factors as a means of clustering of interestingness measures of association rules

DOI10.1007/s10472-013-9370-xzbMath1429.62208OpenAlexW1979037486WikidataQ62610111 ScholiaQ62610111MaRDI QIDQ2248543

Jan Outrata, Sylvie Guillaume, Dhouha Grissa, Engelbert Mephu Nguifo, Radim Bělohlávek

Publication date: 26 June 2014

Published in: Annals of Mathematics and Artificial Intelligence (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10472-013-9370-x

zbMATH Keywords

clusteringformal concept analysisinterestingness measuresBoolean factor analysisassociation rules measures

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Measures of association (correlation, canonical correlation, etc.) (62H20)

Related Items (3)

Formal concept analysis approach to understand digital evidence relationships ⋮ From-below Boolean matrix factorization algorithm based on MDL ⋮ An incremental recomputation of from-below Boolean matrix factorization

Cites Work

• Discovery of optimal factors in binary data via a novel method of matrix decomposition
• The GUHA method and its meaning for data mining
• Mechanizing hypothesis formation. Mathematical foundations for a general theory
• On selecting interestingness measures for association rules: user oriented description and multiple criteria decision aid
• STUDYING INTEREST MEASURES FOR ASSOCIATION RULES THROUGH A LOGICAL MODEL
• Discovery Science
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