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Constrained frequent pattern mining: a pattern-growth view
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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Metadata
- Authors
- Pei, J., & Han, J.
- Affiliations
- Simon Fraser University (CA)
- University of Illinois Urbana-Champaign (US)
- ORCID
- 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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