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A Comparative Analysis of Algorithms for Mining Frequent Itemsets

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Databases and Information Systems (DB&IS 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 615))

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Abstract

Finding frequent sets of items was first considered critical to mining association rules in the early 1990s. In the subsequent two decades, there have appeared numerous new methods of finding frequent itemsets, which underlines the importance of this problem. The number of algorithms has increased, thus making it more difficult to select proper one for a particular task and/or a particular type of data. This article analyses and compares the twelve most widely used algorithms for mining association rules. The choice of the most efficient of the twelve algorithms is made not only on the basis of available research data, but also based on empirical evidence. In addition, the article gives a detailed description of some approaches and contains an overview and classification of algorithms.

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Authors and Affiliations

  1. Saint Petersburg State University, St. Petersburg, Russia

    Vyacheslav Busarov, Natalia Grafeeva & Elena Mikhailova

Authors
  1. Vyacheslav Busarov
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  2. Natalia Grafeeva
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  3. Elena Mikhailova
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Corresponding author

Correspondence to Vyacheslav Busarov .

Editor information

Editors and Affiliations

  1. University of Latvia, Riga, Latvia

    Guntis Arnicans

  2. University of Latvia, Riga, Latvia

    Vineta Arnicane

  3. University of Latvia, Riga, Latvia

    Juris Borzovs

  4. University of Latvia, Riga, Latvia

    Laila Niedrite

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© 2016 Springer International Publishing Switzerland

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Busarov, V., Grafeeva, N., Mikhailova, E. (2016). A Comparative Analysis of Algorithms for Mining Frequent Itemsets. In: Arnicans, G., Arnicane, V., Borzovs, J., Niedrite, L. (eds) Databases and Information Systems. DB&IS 2016. Communications in Computer and Information Science, vol 615. Springer, Cham. https://doi.org/10.1007/978-3-319-40180-5_10

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  • DOI: https://doi.org/10.1007/978-3-319-40180-5_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-40179-9

  • Online ISBN: 978-3-319-40180-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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