Cited by 133Data Mining Algorithms and ApplicationsData Mining and Machine Learning ApplicationsPrivacy-Preserving Technologies in Data

Constrained frequent pattern mining: a pattern-growth view

Pei, Jian, Han, Jiawei·2002·ACM SIGKDD Explorations Newsletter

Abstract

It has been well recognized that frequent pattern mining plays an essential role in many important data mining tasks. However, frequent pattern mining often generates a very large number of patterns and rules, which reduces not only the efficiency but also the effectiveness of mining. Recent work has highlighted the importance of the constraint-based mining paradigm in the context of mining frequent itemsets, associations, correlations, sequential patterns, and many other interesting patterns in large databases.Recently, we developed efficient pattern-growth methods for frequent pattern mining. Interestingly, pattern-growth methods are not only efficient but also effective in mining with various constraints. Many tough constraints which cannot be handled by previous methods can be pushed deep into the pattern-growth mining process. In this paper, we overview the principles of pattern-growth methods for constrained frequent pattern mining and sequential pattern mining. Moreover, we explore the power of pattern-growth methods towards mining with tough constraints and highlight some interesting open problems.

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Crossref · 10.1145/568574.568580

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36 of 36 references verified0 citation errors · 0 not checkable

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Authors
Pei, J., & Han, J.
Affiliations
  • Simon Fraser University (CA)
  • University of Illinois Urbana-Champaign (US)
Venue
ACM SIGKDD Explorations Newsletter
Published
2002-06
Volume
4
Issue
1
Pages
31-39
ISSN
  • 1931-0145 (ISSN-L)
  • 1931-0153
OpenAlex
W2044306058
Cited by
133
References
37
Citations by year
2025: 1 · 2023: 1 · 2022: 1 · 2021: 4 · 2020: 2 · 2019: 6 · 2018: 7 · 2017: 9 · 2016: 5 · 2015: 7 · 2014: 10 · 2013: 15 · 2012: 5
FWCI
22.96
Type
journal article
Language
en
Open access
closed
Locations
ACM SIGKDD Explorations Newsletter (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Information Systems › Data Mining Algorithms and Applications
  • Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
  • Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
Keywords
frequent itemset mining, sequential pattern mining, constraint-based mining, association rule mining, large databases

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