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Pattern-Growth Methods

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Encyclopedia of Database Systems

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. Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Hong Kong, China

    Hong Cheng

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

    Jiawei Han

Authors
  1. Hong Cheng
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  2. Jiawei Han
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Corresponding author

Correspondence to Hong Cheng .

Editor information

Editors and Affiliations

  1. Georgia Institute of Technology College of Computing, Atlanta, GA, USA

    Ling Liu

  2. University of Waterloo School of Computer Science, Waterloo, ON, Canada

    M. Tamer Özsu

Section Editor information

  1. School of Computing Science, Simon Fraser Univ., Burnaby, British Columbia, Canada

    Jian Pei

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Cheng, H., Han, J. (2018). Pattern-Growth Methods. In: Liu, L., Özsu, M.T. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8265-9_263

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