Encyclopedia of Database Systems

2018 Edition
| Editors: Ling Liu, M. Tamer Özsu

Pattern-Growth Methods

  • Hong Cheng
  • Jiawei Han
Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-8265-9_263

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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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Systems Engineering and Engineering ManagementThe Chinese University of Hong KongHong KongChina
  2. 2.University of Illinois at Urbana-ChampaignUrbanaUSA

Section editors and affiliations

  • Jian Pei
    • 1
  1. 1.School of Computing ScienceSimon Fraser Univ.BurnabyCanada