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GA-Apriori: Combining Apriori Heuristic and Genetic Algorithms for Solving the Frequent Itemsets Mining Problem

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Book cover Trends and Applications in Knowledge Discovery and Data Mining (PAKDD 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10526))

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Abstract

Finding frequent itemsets is a popular data mining problem, aiming to extract hidden patterns from a transactional database. Several bio-inspired approaches to solve this problem have been proposed to overcome the poor performance of exact algorithms, such as Apriori and FPGrowth. Approaches based on genetic algorithms are among the most efficient ones from the point of view of runtime performance, but they are still inefficient in terms of solution’s quality, i.e., the number of frequent itemsets discovered. To deal with this issue, we propose in this paper a new genetic algorithm for finding frequent itemsets called GA-Apriori, in which the crossover and mutation operators are defined by taking into account the Apriori heuristic principle. The results of our evaluation show that GA-Apriori outperforms other approaches to frequent itemset mining based on genetic algorithms, especially when dealing with large instances. The experiments also show that GA-Apriori is competitive with exact approaches in terms of the number of frequent itemsets discovered.

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Authors and Affiliations

  1. Ulsan National Institute of Science and Technology (UNIST), 50 UNIST-gil, Ulju-gun, Ulsan, 44949, Republic of Korea

    Youcef Djenouri & Marco Comuzzi

Authors
  1. Youcef Djenouri
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  2. Marco Comuzzi
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Corresponding author

Correspondence to Youcef Djenouri .

Editor information

Editors and Affiliations

  1. Seoul National University, Seoul, Korea (Republic of)

    U Kang

  2. School of Information Systems, Singapore Management University, Singapore, Singapore

    Ee-Peng Lim

  3. Chinese University of Hong Kong, Hong Kong, China

    Jeffrey Xu Yu

  4. Kangwon National University, Chuncheon, Korea (Republic of)

    Yang-Sae Moon

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Djenouri, Y., Comuzzi, M. (2017). GA-Apriori: Combining Apriori Heuristic and Genetic Algorithms for Solving the Frequent Itemsets Mining Problem. In: Kang, U., Lim, EP., Yu, J., Moon, YS. (eds) Trends and Applications in Knowledge Discovery and Data Mining. PAKDD 2017. Lecture Notes in Computer Science(), vol 10526. Springer, Cham. https://doi.org/10.1007/978-3-319-67274-8_13

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  • DOI: https://doi.org/10.1007/978-3-319-67274-8_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67273-1

  • Online ISBN: 978-3-319-67274-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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