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Article Dans Une Revue International Journal On Advances in Software Année : 2012

Combining Explicitness and Classifying Performance via MIDOVA Lossless Representation for Qualitative Datasets

Résumé

Basically, MIDOVA lists the relevant combinations of K boolean variables, thus giving rise to an appropriate expansion of the original set of variables, well-fitted to for a number of data mining tasks. MIDOVA takes into account the presence as well as the absence of items. The building of level-k itemsets starting from level-k-1 ones relies on the concept of residue, which entails the potential of an itemset to create higher-order non-trivial associations. We assess the value of such a representation by presenting an application to three well-known classification tasks: the resulting success proves that our objective of extracting the relevant interactions hidden in the data, and only these ones, has been hit.
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Dates et versions

hal-00596718 , version 1 (29-05-2011)

Identifiants

  • HAL Id : hal-00596718 , version 1

Citer

Martine Cadot, Alain Lelu. Combining Explicitness and Classifying Performance via MIDOVA Lossless Representation for Qualitative Datasets. International Journal On Advances in Software, 2012, 5 (1&2), pp.1-16. ⟨hal-00596718⟩
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