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Knowledge and Information Systems

, Volume 53, Issue 2, pp 439–477 | Cite as

A formal series-based unification of the frequent itemset mining approaches

  • Slimane Oulad-NaouiEmail author
  • Hadda Cherroun
  • Djelloul Ziadi
Regular Paper

Abstract

Over the last two decades, a great deal of work has been devoted to the algorithmic aspects of the frequent itemset (FI) mining problem, leading to a phenomenal number of algorithms and associated implementations, each of which claims supremacy. Meanwhile, it is generally well agreed that developing a unifying theory is one of the most important issues in data mining research. Hence, our primary motivation for this work is to introduce a high-level formalism for this basic problem, which induces a unified vision of the algorithmic approaches presented so far. The key distinctive feature of the introduced model is that it combines, in one fashion, both the qualitative and the quantitative aspects of this basic problem. In this paper, we propose a new model for the FI-mining task based on formal series. In fact, we encode the itemsets as words over a sorted alphabet and express this problem by a formal series over the counting semiring \((\mathbb N,+,\times ,0,1)\), whose range represents the itemsets, and the coefficients are their supports. The aim is threefold: First, to define a clear, unified and extensible theoretical framework through which we can state the main FI-approaches. Second, to prove a convenient connection between the determinization of the acyclic weighted automaton that represents a transaction dataset and the computation of the associated collection of FI. Finally, to devise a first algorithmic transcription, baptized Wafi, of our model by means of weighted automata, which we evaluate against representative leading algorithms. The obtained results show the suitability of our formalism.

Keywords

Data mining Frequent itemsets Formal series Weighted automata Algorithms Unification 

Notes

Acknowledgements

we would like to sincerely thank the anonymous reviewers for their time devoted to thoughtfully reading our manuscript, and for their various insightful remarks and comments which helped us to improve the quality of the paper. This work was supported by Algeria/South Africa joint project under code: A/AS-2013-002.

Supplementary material

10115_2017_1048_MOESM1_ESM.zip (727 kb)
Supplementary material 1 (zip 728 KB)

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Copyright information

© Springer-Verlag London 2017

Authors and Affiliations

  1. 1.Laboratoire d’Informatique et de MathématiquesUniversité Amar TelidjiLaghouatAlgeria
  2. 2.Laboratoire LITIS, EA 4108Normandie UniversitéRouenFrance

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