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A Survey of High Utility Pattern Mining Algorithms for Big Data

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High-Utility Pattern Mining

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

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Abstract

High utility pattern mining is an essential data mining task with a goal of extracting knowledge in the form of patterns. A pattern is called a high utility pattern if its utility, defined based on a domain objective, is no less than a minimum utility threshold. Several high utility pattern mining algorithms have been proposed in the last decade, yet most do not scale to the type of data we are nowadays dealing with, the so-called big data. This chapter aims to provide a comprehensive overview and a big-picture to readers of high utility pattern mining in big data. We first review the problem of high utility pattern mining and related technologies, such as Apache Spark, Apache Hadoop, and parallel and distributed processing. Then, we review recent advances in parallel and scalable high utility pattern mining, analyzing them through the big data point of view and indicate challenges to design parallel high utility pattern mining algorithms. In particular, we study two common types of high utility patterns, i.e., high utility itemsets (HUIs) and high utility sequential patterns (HUSPs). The chapter is concluded with a discussion of open problems and future directions.

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Notes

  1. 1.

    http://wiki.apache.org/hadoop.

  2. 2.

    http://mesos.apache.org.

  3. 3.

    https://www.ibm.com/developerworks/servicemanagement/tc/pcs/index.html.

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Author information

Authors and Affiliations

  1. Ted Rogers School of Information Technology Management, Ryerson University, Toronto, Canada

    Morteza Zihayat & Mehdi Kargar

  2. Faculty of Science, University of Ontario Institute of Technology, Oshawa, Canada

    Jaroslaw Szlichta

Authors
  1. Morteza Zihayat
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  2. Mehdi Kargar
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  3. Jaroslaw Szlichta
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Corresponding author

Correspondence to Morteza Zihayat .

Editor information

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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Zihayat, M., Kargar, M., Szlichta, J. (2019). A Survey of High Utility Pattern Mining Algorithms for Big Data. 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_3

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