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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Key semantics extraction by dependency tree mining
Morinaga, S., Arimura, H., Ikeda, T., Sakao, Y., & Akamine, S. (2005) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1081870.1081954 · Cited by 5 · see citing works
Efficient mining of correlated sequential patterns based on null hypothesis
Lin, C. X., Ji, M., Danilevsky, M., & Han, J. (2012) · doi:10.1145/2389656.2389660 · Cited by 5 · see citing works
Self-citationKifer, D., Gehrke, J. E., Bucilă, C., & White, W. M. (2006) · Lecture notes in computer science · doi:10.1007/11615576_11 · Cited by 5 · see citing works
Pattern matching with wildcards and length constraints using maximum network flow
Arslan, A. N., He, D., He, Y. H., & Wu, X. (2015) · Journal of Discrete Algorithms · doi:10.1016/j.jda.2015.08.003 · Cited by 4 · see citing works
Efficient Algorithm for Mining High Utility Pattern Considering Length Constraints
Singh, K., & Biswas, B. (2019) · International Journal of Data Warehousing and Mining · doi:10.4018/ijdwm.2019070101 · Cited by 4 · see citing works
An improved neighborhood-restricted association rule-based recommender system
Kiran, R. U., & Kitsuregawa, M. (2013) · Australasian Database Conference · Cited by 4 · see citing works
Similar interest clustering and partial back‐propagation‐based recommendation in digital library
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A Way to Understand Various Patterns of Data Mining Techniques for Selected DomainsPreprint
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Analytical Study of Association Rule Mining Methods in Data Mining
Patel, B. M., Bhemwala, V. H., & Patel, D. A. R. (2018) · International Journal of Scientific Research in Computer Science Engineering and Information Technology · doi:10.32628/cseit1833244 · Cited by 3 · see citing works
Mining Frequent Patterns in Print Logs with Semantically Alternative Labels
Li, X., Zhang, L., Chen, E., Zong, Y., & Xu, G. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-53917-6_10 · Cited by 3 · see citing works
SIMPLE FUZZY GRID PARTITION FOR MINING MULTIPLE-LEVEL FUZZY SEQUENTIAL PATTERNS
Hu, Y. (2007) · Cybernetics & Systems · doi:10.1080/01969720601139124 · Cited by 3 · see citing works
Improvement on the Constrained Association Rule Mining Algorithm of Separate
Yuan, X., Hua-long, X., & Chen, S. (2007) · International Conference on Digital Information Management · doi:10.1109/icdim.2007.369354 · Cited by 2 · see citing works
Towards the Integration of Constrained Mining with Star Schemas
Silva, A., & Antunes, C. (2013) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2013.102 · Cited by 2 · see citing works
Towards Addressing the Coverage Problem in Association Rule-Based Recommender Systems
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Two Decades of Pattern Mining: Principles and Methods
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Pushing Constraints into a Pattern-Tree
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New algorithms for pattern matching with wildcards and length constraints
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FREQUENT CORRELATED PERIODIC PATTERN MINING FOR LARGE VOLUME SET USING TIME SERIES DATA
Karthik, G. M., & Karthik, S. S. (2014) · Journal of Computer Science · doi:10.3844/jcssp.2014.2105.2116 · Cited by 2 · see citing works
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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