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Big Data Frequent Pattern Mining

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Abstract

Frequent pattern mining is an essential data mining task, with a goal of discovering knowledge in the form of repeated patterns. Many efficient pattern mining algorithms have been discovered in the last two decades, yet most do not scale to the type of data we are presented with today, the so-called “Big Data”. Scalable parallel algorithms hold the key to solving the problem in this context. In this chapter, we review recent advances in parallel frequent pattern mining, analyzing them through the Big Data lens. We identify three areas as challenges to designing parallel frequent pattern mining algorithms: memory scalability, work partitioning, and load balancing. With these challenges as a frame of reference, we extract and describe key algorithmic design patterns from the wealth of research conducted in this domain.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, University of Minnesota, Minneapolis, USA

    David C. Anastasiu, Jeremy Iverson, Shaden Smith & George Karypis

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  1. David C. Anastasiu
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  2. Jeremy Iverson
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  3. Shaden Smith
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  4. George Karypis
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Correspondence to David C. Anastasiu .

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Editors and Affiliations

  1. IBM, Yorktown Heights, New York, USA

    Charu C. Aggarwal

  2. University of Illinois at Urbana-Champaign, Urbana, Illinois, USA

    Jiawei Han

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Anastasiu, D., Iverson, J., Smith, S., Karypis, G. (2014). Big Data Frequent Pattern Mining. In: Aggarwal, C., Han, J. (eds) Frequent Pattern Mining. Springer, Cham. https://doi.org/10.1007/978-3-319-07821-2_10

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  • DOI: https://doi.org/10.1007/978-3-319-07821-2_10

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