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Pattern-Growth Methods

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Encyclopedia of Database Systems

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Definition

Pattern-growth is one of several influential frequent pattern mining methodologies, where a pattern (e.g., an itemset, a subsequence, a subtree, or a substructure) is frequent if its occurrence frequency in a database is no less than a specified minimum_support threshold. The (frequent) pattern-growth method mines the data set in a divide-and-conquer way: It first derives the set of size-1 frequent patterns, and for each pattern p, it derives p’s projected (or conditional) database by data set partitioning and mines the projected database recursively. Since the data set is decomposed progressively into a set of much smaller, pattern-related projected data sets, the pattern-growth method effectively reduces the search space and leads to high efficiency and scalability.

Historical Background

Frequent itemset mining was first introduced as an essential subtask of association rule mining by Agrawal et al. [1]. A candidate set generation-and-test approach, represented by the...

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Recommended Reading

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Author information

Authors and Affiliations

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

    Hong Cheng

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

    Jiawei Han

Authors
  1. Hong Cheng
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  2. Jiawei Han
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Editor information

Editors and Affiliations

  1. College of Computing, Georgia Institute of Technology, 266 Ferst Drive, 30332-0765, Atlanta, GA, USA

    LING LIU (Professor) (Professor)

  2. Database Research Group David R. Cheriton School of Computer Science, University of Waterloo, 200 University Avenue West, N2L 3G1, Waterloo, ON, Canada

    M. TAMER ÖZSU (Professor and Director, University Research Chair) (Professor and Director, University Research Chair)

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© 2009 Springer Science+Business Media, LLC

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Cheng, H., Han, J. (2009). Pattern-Growth Methods. In: LIU, L., ÖZSU, M.T. (eds) Encyclopedia of Database Systems. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-39940-9_263

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