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[PNS07] Combined use of association rules mining and clustering methods to find relevant links between binary rare attributes in a large data set

Revue Internationale avec comité de lecture : Journal Computational Statistics & Data Analysis, vol. 52(1), pp. 596-613, 2007
motcle:
Résumé: 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.

Equipe: msdma

BibTeX

@article {
PNS07,
title="{Combined use of association rules mining and clustering methods to find relevant links between binary rare attributes in a large data set}",
author="M. Plasse and N. Niang Keita and G. Saporta and A. Villeminot and L. Leblond",
journal="Computational Statistics & Data Analysis",
year=2007,
volume=52,
number=1,
pages="596-613",
}