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
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
Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data
Karpatne, A., Atluri, G., Faghmous, J. H., Steinbach, M., Banerjee, A., Ganguly, A., Shekhar, S., Samatova, N., & Kumar, V. (2017) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2017.2720168 · Cited by 1,752
Sequential pattern mining -- approaches and algorithms
Mooney, C. H., & Roddick, J. F. (2013) · ACM Computing Surveys · doi:10.1145/2431211.2431218 · Cited by 325
Discovering data quality rules
Chiang, F., & Miller, R. J. (2008) · Proceedings of the VLDB Endowment · doi:10.14778/1453856.1453980 · Cited by 267
Association Rule Discovery With the Train and Test Approach for Heart Disease Prediction
ORDÓÑEZ, C. R. (2006) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2006.864475 · Cited by 214
Mining Nonambiguous Temporal Patterns for Interval-Based Events
Wu, S.-Y., & Chen, Y.-L. (2007) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2007.190613 · Cited by 181
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
