Combined use of association rules mining and clustering methods to find relevant links between binary rare attributes in a large data set

DOI10.1016/j.csda.2007.02.020zbMath1452.62460OpenAlexW2122182414MaRDI QIDQ1020849

Laurent Leblond, Alexandre Villeminot, Ndeye Niang, Marie Plasse, Gilbert Saporta

Publication date: 2 June 2009

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2007.02.020

zbMATH Keywords

variable clusteringlarge sparse matrixassociation rules miningbinary attributesrule relevancy index

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Related Items (10)

Critical appraisal of jointness concepts in Bayesian model averaging: evidence from life sciences, sociology, and other scientific fields ⋮ Interestingness Measures for Association Rules within Groups ⋮ Information fusion from multiple databases using meta-association rules ⋮ Scenario-based analysis for discovering relations among interestingness measures ⋮ Hierarchical clustering of continuous variables based on the empirical copula process and permutation linkages ⋮ Behavior-based clustering and analysis of interestingness measures for association rule mining ⋮ Editorial: Statistical learning methods including dimensionality reduction ⋮ Combined use of association rules mining and clustering methods to find relevant links between binary rare attributes in a large data set ⋮ Editorial: Some recent trends in applied stochastic modeling and multidimensional data analysis ⋮ Exploratory data analysis leading towards the most interesting simple association rules

Uses Software

• SAS
• SAS/STAT

Cites Work

• Combined use of association rules mining and clustering methods to find relevant links between binary rare attributes in a large data set
• Exploratory data analysis leading towards the most interesting simple association rules
• Metric and Euclidean properties of dissimilarity coefficients
• Clustering of Variables Around Latent Components
• Unnamed Item
• Unnamed Item

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