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A Comparative Study of SAT-Based Itemsets Mining

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Research and Development in Intelligent Systems XXXIII (SGAI 2016)

Abstract

Mining frequent itemsets from transactional datasets is a well known problem. Thus, various methods have been studied to deal with this issue. Recently, original proposals have emerged from the cross-fertilization between data mining and artificial intelligence. In these declarative approaches, the itemset mining problem is modeled either as a constraint network or a propositional formula whose models correspond to the patterns of interest. In this paper, we focus on the propositional satisfiability based itemset mining framework. Our main goal is to enhance the efficiency of SAT model enumeration algorithms. This issue is particularly crucial for the scalability and competitiveness of such declarative itemset mining approaches. In this context, we deeply analyse the effect of the different SAT solver components on the efficiency of the model enumeration problem. Our analysis includes the main components of modern SAT solvers such as restarts, activity based variable ordering heuristics and clauses learning mechanism. Through extensive experiments, we show that these classical components play an essential role in such procedure to improve the performance by pushing forward the efficiency of SAT solvers. More precisely, our experimental evaluation includes a comparative study in enumerating all the models corresponding to the closed frequent itemsets. Additionally, our experimental analysis is extended to include the Top-k itemset mining problem.

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Notes

  1. 1.

    FIMI: http://fimi.ua.ac.be/data/.

  2. 2.

    CP4IM: http://dtai.cs.kuleuven.be/CP4IM/datasets/.

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Authors and Affiliations

  1. CRIL CNRS UMR 8188, University of Artois, Lens, France

    Imen Ouled Dlala, Said Jabbour & Lakhdar Sais

  2. LARODEC, High Institute of Management, University of Tunis, Tunis, Tunisia

    Boutheina Ben Yaghlane

Authors
  1. Imen Ouled Dlala
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  2. Said Jabbour
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  3. Lakhdar Sais
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  4. Boutheina Ben Yaghlane
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Corresponding author

Correspondence to Said Jabbour .

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Editors and Affiliations

  1. School of Computing, University of Portsmouth, Portsmouth, Hampshire, United Kingdom

    Max Bramer

  2. School of Computing, Engineering and Mathematics, University of Brighton, Brighton, East Sussex, United Kingdom

    Miltos Petridis

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Dlala, I.O., Jabbour, S., Sais, L., Yaghlane, B.B. (2016). A Comparative Study of SAT-Based Itemsets Mining. In: Bramer, M., Petridis, M. (eds) Research and Development in Intelligent Systems XXXIII. SGAI 2016. Springer, Cham. https://doi.org/10.1007/978-3-319-47175-4_3

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  • DOI: https://doi.org/10.1007/978-3-319-47175-4_3

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-47175-4

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