The DOI matches its Crossref record; 36 of the 37 references it cites were found in a registry or an index.
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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Citations
Cites
Fast algorithms for mining association rules
Agrawal, R., & Srikant, R. (1998) · Cited by 11,362
Fast Algorithms for Mining Association Rules in Large Databases
Agrawal, R., & Srikant, R. (1994) · Very Large Data Bases · Cited by 9,454
Mining frequent patterns without candidate generation
Han, J., Pei, J., & Yin, Y. (2000) · ACM SIGMOD Record · doi:10.1145/335191.335372 · Cited by 6,402
Self-citationAgrawal, R., & Srikant, R. (2002) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde.1995.380415 · Cited by 5,150
Mining frequent patterns without candidate generation
Han, J., Pei, J., & Yin, Y. (2000) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/342009.335372 · Cited by 3,255
Self-citationIntegrating classification and association rule mining
Liu, B., Hsu, W., & Ma, Y. (1998) · Cited by 2,247
PrefixSpan,: mining sequential patterns efficiently by prefix-projected pattern growth
Pei, J., Han, J., Mortazavi-Asl, B., Pinto, H. M., Chen, Q., Dayal, U., & Hsu, M. (2005) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde.2001.914830 · Cited by 1,816
Self-citationAn Efficient Algorithm for Mining Association Rules in Large Databases
Savasere, A., Omiecinski, E. R., & Navathe, S. B. (1995) · Cited by 1,601
Rock: A robust clustering algorithm for categorical attributes
Guha, S., Rastogi, R. R., & Shim, K. (2000) · Information Systems · doi:10.1016/s0306-4379(00)00022-3 · Cited by 1,529
Discovery of Frequent Episodes in Event Sequences
Mannila, H., Toivonen, H. T. T., & Verkamo, A. I. (1997) · Data Mining and Knowledge Discovery · doi:10.1023/a:1009748302351 · Cited by 1,491
Flagged first, then by citations
1 of the 37 references couldn’t be matched to a known paper.
Research works printed but not linked: 6
- 10.5555/645480.655281 · DOI
- 10.5555/645484.656386 · DOI
- 10.5555/846219.847312 · DOI
- 10.5555/846218.847264 · DOI
- 10.5555/645482.653287 · DOI
- J. Pei , J. Han , and W. Wang . Constraint-based sequential pattern mining in large databases . In Submitted for publication , May 2002 . J. Pei, J. Han, and W. Wang. Constraint-based sequential pattern mining in large databases. In Submitted for publication, May 2002.
Cited by
Sorted by citations
Items 101–133 of 133
A Guided FP-growth algorithm for fast mining of frequent itemsets from big data.
Shabtay, L., Yaari, R., & Dattner, I. (2018) · Cited by 2
Designing and Mining Web Applications
Meo, R., & Matera, M. (2007) · IGI Global eBooks · doi:10.4018/978-1-59904-228-2.ch008 · Cited by 2
A data mining approach to improve the automated quality of data
Alkharboush, N. (2014) · QUT ePrints (Queensland University of Technology) · Cited by 2
Tree mining: Equivalence classes for candidate generation
López, F., Laurent, A., Teisseire, M., & Pascal Poncelet, B. (2009) · Intelligent Data Analysis · doi:10.3233/ida-2009-0381 · Cited by 1
Data trend mining for predictive systems design
Tucker, C. S. (2011) · Illinois Digital Environment for Access to Learning and Scholarship (University of Illinois at Urbana-Champaign) · Cited by 1
Towards efficient discovery of frequent patterns with relative support
Kiran, R. U., & Kitsuregawa, M. (2012) · doi:10.5555/2694443.2694460 · Cited by 1
Nedjar, S., Casali, A., Cicchetti, R., & Lakhal, L. (2006) · Ingénierie des systèmes d information · doi:10.3166/isi.11.6.11-31 · Cited by 1
Retracted on September 10, 2025: Similarity Association Pattern Mining in Transaction Databases
Phridviraj, M. S. B., Guru Rao, C. V., Radhakrishna, V., & Cheruvu, A. (2021) · International Conference on Data Science, E-Learning and Information Systems (DATA) · doi:10.1145/3460620.3460752 · Cited by 1
RetractedInterpretation of association rules with multi-tier granule mining
Wu, J. (2014) · Cited by 1
Data quality through active constraint discovery and maintenance
Miller, R. J., & Chiang, F. (2012) · TSpace (University of Toronto) · Cited by 1
Motifs corrélés rares : Caractérisation et nouvelles représentations concisesPreprint
Bouasker, S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.07116 · Cited by 1
Building A Data Mining Query Optimizer.
Gopalan, R. P., Nuruddin, T., & Sucahyo, Y. G. (2002) · Australasian Data Mining Conference · Cited by 1
Nijssen, S. (2010) · Encyclopedia of Machine Learning · doi:10.1007/978-0-387-30164-8_164 · Cited by 1
Discovering frequent pattern pairs
ORDÓÑEZ, C. R., & Chen, Z. (2013) · Intelligent Data Analysis · doi:10.3233/ida-130614
Managing and analyzing phylogenetic databases
Deepak, A. (2013) · Iowa State University Digital Repository (Iowa State University)
Towards efficient discovery of coverage patterns in transactional databases
Kiran, R. U., Toyoda, M., & Kitsuregawa, M. (2013) · International Conference on Scientific and Statistical Database Management · doi:10.1145/2484838.2484850
Mining circumstance-oriented association rules using singular value decomposition technique
Chen, Y., & Chan, K.-P. (2005) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/icsmc.2004.1400827
Improvement on theConstrained Association RuleMiningAlgorithm of Separate
Yuan, X., & Hua-long, X. (2006)
Introduction to Pattern Mining
Ventura, S., & Luna, J. M. (2016) · Springer eBooks · doi:10.1007/978-3-319-33858-3_1
Frequent Mining on XML Documents
Kutty, S. (2009) · IGI Global eBooks · doi:10.4018/978-1-59904-990-8.ch014
Nijssen, S. (2017) · Encyclopedia of Machine Learning and Data Mining · doi:10.1007/978-1-4899-7687-1_164
Fakih, M. F., Yuniarno, E. M., & Nugroho, S. M. S. (2017) · Kinetik Game Technology Information System Computer Network Computing Electronics and Control · doi:10.22219/kinetik.v2i3.46
GRAPE: Parallel Graph Query Engine
Xu, J. (2017) · ERA
Constrained Gene Block Discovery and Its Application to Prokaryotic Genomes
Engel, J., Veksler‐Lublinsky, I., & Ziv-Ukelson, M. (2019) · Journal of Computational Biology · doi:10.1089/cmb.2019.0096
Flexible Pattern Discovery and AnalysisPreprint
Chen, C., Chen, L., & Gan, W. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.12218
Flexibly Mining Better Patterns
Huang, G., Gan, W., Li, L., Gu, T., & Chen, J. (2022) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata55660.2022.10021132
Data Warehousing for Association Mining
Li, Y. (2009) · IGI Global eBooks · doi:10.4018/978-1-60566-010-3.ch093
Cheng, S., Hijazi, A., Konak, J., Erera, A. L., & Hentenryck, P. V. (2025) · Proceedings · doi:10.1109/icdm65498.2025.00024
Dauxais, Y. (2018) · doi:10.70675/46d8545bz103cz4620zb6f7z5c7bc3f81eae
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
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.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
