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Communication Dans Un Congrès Année : 2014

Mining frequent itemsets in evidential database

Résumé

Mining frequent patterns is widely used to discover knowledge from a database. It was originally applied on Market Basket Analysis (MBA) problem which represents the Boolean databases. In those databases, only the existence of an article (item) in a transaction is defined. However, in real-world application, the gathered information generally suffer from imperfections. In fact, a piece of information may contain two types of imperfection: imprecision and uncertainty. Recently, a new database representing and integrating those two types of imperfection were introduced: Evidential Database. Only few works have tackled those databases from a data mining point of view. In this work, we aim to discuss evidential itemset's support. We improve the complexity of state of art methods for support's estimation. We also introduce a new support measure gathering fastness and precision. The proposed methods are tested on several constructed evidential databases showing performance improvement.
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Dates et versions

hal-03649721 , version 1 (22-04-2022)

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Ahmed Samet, Eric Lefevre, Sadok Ben Yahia. Mining frequent itemsets in evidential database. International Conference Knowledge and Systems Engineering, KSE'2013, Oct 2013, Hanoi, Vietnam. pp.377-388, ⟨10.1007/978-3-319-02821-7_33⟩. ⟨hal-03649721⟩
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