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Metaheuristics for Frequent and High-Utility Itemset Mining

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Book cover High-Utility Pattern Mining

Part of the book series: Studies in Big Data ((SBD,volume 51))

Abstract

Metaheuristics are often used to solve combinatorial problems. They can be viewed as general purpose problem-solving approaches based on stochastic methods, which explore very large search spaces to find near-optimal solutions in a reasonable time. Some metaheuristics are inspired by biological and physical phenomenons. During the last two decades, two population-based methods named evolutionary algorithms and swarm intelligence have shown high efficiency compared to many other metaheuristics. Frequent Itemset Mining (FIM) and High Utility Itemset Mining (HUIM) are the process of extracting useful frequent and high utility itemsets from a given transactional database. Solving FIM and HUIM problems can be very time consuming, especially when dealing with large-scale data. To deal with this issue, different metaheuristic-based methods were developed. In this chapter, we study the application of metaheuristics to FIM and HUIM. Several metaheuristics have been presented, based on evolutionary or swarm intelligence algorithms, such as genetic algorithms, particle swarm optimization, ant colony optimization and bee swarm optimization.

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Authors and Affiliations

  1. IMADA, Southern Denmark University, Odense, Denmark

    Youcef Djenouri

  2. School of Humanities and Social Sciences, Harbin Institute of Technology (Shenzhen), Shenzhen, China

    Philippe Fournier-Viger

  3. RIMA, University of Science and Technology Houari Boumediene (USTHB), Algiers, Algeria

    Asma Belhadi

  4. Department of Computing Mathematics and Physics, Western Norway University of Applied Sciences (HVL), Bergen, Norway

    Jerry Chun-Wei Lin

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  1. Youcef Djenouri
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  2. Philippe Fournier-Viger
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  3. Asma Belhadi
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  4. Jerry Chun-Wei Lin
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Correspondence to Youcef Djenouri .

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Editors and Affiliations

  1. Harbin Institute of Technology (Shenzhen), Shenzhen, China

    Philippe Fournier-Viger

  2. Western Norway University of Applied Sciences, Bergen, Norway

    Jerry Chun-Wei Lin

  3. Université du Québec à Montréal, Montreal, QC, Canada

    Roger Nkambou

  4. Ho Chi Minh City University of Technology, Ho Chi Minh City, Vietnam

    Bay Vo

  5. National Chiao Tung University, Hsinchu, Taiwan

    Vincent S. Tseng

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Djenouri, Y., Fournier-Viger, P., Belhadi, A., Chun-Wei Lin, J. (2019). Metaheuristics for Frequent and High-Utility Itemset Mining. In: Fournier-Viger, P., Lin, JW., Nkambou, R., Vo, B., Tseng, V. (eds) High-Utility Pattern Mining. Studies in Big Data, vol 51. Springer, Cham. https://doi.org/10.1007/978-3-030-04921-8_10

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