Skip to Main content Skip to Navigation
Journal articles

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

Abstract : A method to analyse links between binary attributes in a large sparse data set is proposed. Initially the variables are clustered to obtain homogeneous clusters of attributes. Association rules are then mined in each cluster. A graphical comparison of some rule relevancy indexes is presented. It is used to extract best rules depending on the application concerned. The proposed methodology is illustrated by an industrial application from the automotive industry with more than 80 000 vehicles each described by more than 3000 rare attributes.
Complete list of metadatas

https://hal.archives-ouvertes.fr/hal-01125291
Contributor : Laboratoire Cedric <>
Submitted on : Friday, March 6, 2015 - 11:05:00 AM
Last modification on : Sunday, March 15, 2020 - 2:32:53 PM

Identifiers

Collections

Citation

Marie Plasse, Ndeye Niang Keita, Gilbert Saporta, Alexandre Villeminot, Laurent Leblond. Combined use of association rules mining and clustering methods to find relevant links between binary rare attributes in a large data set. Computational Statistics and Data Analysis, Elsevier, 2007, 52 (1), pp.596-613. ⟨10.1016/j.csda.2007.02.020⟩. ⟨hal-01125291⟩

Share

Metrics

Record views

146