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Exploratory data analysis leading towards the most interesting simple association rules

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  • Iodice D'Enza, Alfonso
  • Palumbo, Francesco
  • Greenacre, Michael

Abstract

Association rules (AR) represent one of the most powerful and largely used approaches to detect the presence of regularities and paths in large databases. Rules express the relations (in terms of co-occurrence) between pairs of items and are defined in two measures: support and confidence. Most techniques for finding AR scan the whole data set, evaluate all possible rules and retain only rules that have support and confidence greater than thresholds, which should be fixed in order to avoid both that only trivial rules are retained and also that interesting rules are not discarded. A multistep approach aims to the identification of potentially interesting items exploiting well-known techniques of multidimensional data analysis. In particular, interesting pairs of items have a well-defined degree of association: an item pair is well defined if its degree of co-occurrence is very high with respect to one or more subsets of the considered set of transactions.

Suggested Citation

  • Iodice D'Enza, Alfonso & Palumbo, Francesco & Greenacre, Michael, 2008. "Exploratory data analysis leading towards the most interesting simple association rules," Computational Statistics & Data Analysis, Elsevier, vol. 52(6), pages 3269-3281, February.
    • Handle: RePEc:eee:csdana:v:52:y:2008:i:6:p:3269-3281
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    References listed on IDEAS

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    1. Plasse, Marie & Niang, Ndeye & Saporta, Gilbert & Villeminot, Alexandre & Leblond, Laurent, 2007. "Combined use of association rules mining and clustering methods to find relevant links between binary rare attributes in a large data set," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 596-613, September.
    2. Michael Greenacre, 2000. "Correspondence analysis of square asymmetric matrices," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 49(3), pages 297-310.
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    Cited by:

    1. Cristina Tortora & Mireille Gettler Summa & Marina Marino & Francesco Palumbo, 2016. "Factor probabilistic distance clustering (FPDC): a new clustering method," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 10(4), pages 441-464, December.

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