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Frequent Itemset Border Approximation by Dualization

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Transactions on Large-Scale Data- and Knowledge-Centered Systems XXVI

Part of the book series: Lecture Notes in Computer Science ((TLDKS,volume 9670))

Abstract

The approach FIBAD is introduced with the purpose of computing approximate borders of frequent itemsets by leveraging dualization and computation of approximate minimal transversals of hypergraphs. The distinctiveness of the FIBAD’s theoretical foundations is the approximate dualization where a new function \(\widetilde{f}\) is defined to compute the approximate negative border. From a methodological point of view, the function \(\widetilde{f}\) is implemented by the method AMTHR that consists of a reduction of the hypergraph and a computation of its minimal transversals. For evaluation purposes, we study the sensibility of FIBAD to AMTHR by replacing this latter by two other algorithms that compute approximate minimal transversals. We also compare our approximate dualization-based method with an existing approach that computes directly, without dualization, the approximate borders. The experimental results show that our method outperforms the other methods as it produces borders that have the highest quality.

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Notes

  1. 1.

    Frequent Itemset Border Approximation by Dualization.

  2. 2.

    Approximate Minimal Transversals by Hypergraph Reduction.

  3. 3.

    Frequent Itemset Mining Implementations, http://fimi.ua.ac.be/data/.

  4. 4.

    http://www.cs.kent.edu/~lliu/sourceCode.html.

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Authors and Affiliations

  1. Aix Marseille Université, CNRS, ENSAM, Université de Toulon, LSIS UMR 7296, 13397, Marseille, France

    Nicolas Durand & Mohamed Quafafou

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  1. Nicolas Durand
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  2. Mohamed Quafafou
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Correspondence to Nicolas Durand .

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Editors and Affiliations

  1. IRIT, Paul Sabatier University, Toulouse, France

    Abdelkader Hameurlain

  2. FAW, University of Linz, Linz, Austria

    Josef Küng

  3. FAW, University of Linz, Linz, Austria

    Roland Wagner

  4. LIAS/ISAE-ENSMA, Chasseneuil, France

    Ladjel Bellatreche

  5. IBM India Research Lab, New Delhi, India

    Mukesh Mohania

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Durand, N., Quafafou, M. (2016). Frequent Itemset Border Approximation by Dualization. In: Hameurlain, A., Küng, J., Wagner, R., Bellatreche, L., Mohania, M. (eds) Transactions on Large-Scale Data- and Knowledge-Centered Systems XXVI. Lecture Notes in Computer Science(), vol 9670. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49784-5_2

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  • DOI: https://doi.org/10.1007/978-3-662-49784-5_2

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