Big data frequent pattern mining

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

Research output: Chapter in Book/Report/Conference proceedingChapter

12 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationFrequent Pattern Mining
PublisherSpringer International Publishing
Pages225-259
Number of pages35
Volume9783319078212
ISBN (Electronic)9783319078212
ISBN (Print)3319078208, 9783319078205
DOIs
StatePublished - Jul 1 2014

Bibliographical note

Publisher Copyright:
© 2014 Springer International Publishing Switzerland. All rights are reserved.

Keywords

  • Data mining
  • Frequent graph mining
  • Frequent pattern mining
  • Frequent sequence mining
  • Load balancing
  • Memory scalability
  • Motif discovery
  • Parallel algorithms
  • Work partitioning

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