The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Frequent item set mining
Abstract
Frequent item set mining is one of the best known and most popular data mining methods. Originally developed for market basket analysis, it is used nowadays for almost any task that requires discovering regularities between (nominal) variables. This paper provides an overview of the foundations of frequent item set mining, starting from a definition of the basic notions and the core task. It continues by discussing how the search space is structured to avoid redundant search, how it is pruned with the a priori property, and how the output is reduced by confining it to closed or maximal item sets or generators. In addition, it reviews some of the most important algorithmic techniques and data structures that were developed to make the search for frequent item sets as efficient as possible. © 2012 Wiley Periodicals, Inc. This article is categorized under:Algorithmic Development > Association Rules Application Areas > Data Mining Software Tools Technologies > Association Rules
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Mining association rules between sets of items in large databases
Agrawal, R., Imieliński, T., & Swami, A. (1993) · Proceedings of the 1993 ACM SIGMOD international conference on Management of data · doi:10.1145/170035.170072 · Cited by 14,907
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
Mining association rules between sets of items in large databases
Agrawal, R., Imieliński, T., & Swami, A. (1993) · ACM SIGMOD Record · doi:10.1145/170036.170072 · Cited by 4,551
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Luna, J. M., Fournier‐Viger, P., & Ventura, S. (2019) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1329 · Cited by 258
Credibility in social media: opinions, news, and health information—a survey
Viviani, M., & Pasi, G. (2017) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1209 · Cited by 221
Fast Open Modification Spectral Library Searching through Approximate Nearest Neighbor Indexing
(2018) · doi:10.1021/acs.jproteome.8b00359 · Cited by 85
Vinoy, S., Meynier, A., Goux, A., Jourdan-Salloum, N., Normand, S., Rabasa-Lhoret, R., Brack, O., Nazare, J.-A., Péronnet, F., & Laville, M. (2017) · Nutrients · doi:10.3390/nu9040318 · Cited by 38
Murakami, K., Livingstone, M. B. E., Shinozaki, N., Sugimoto, M., Fujiwara, A., Masayasu, S., & Sasaki, S. (2020) · Nutrients · doi:10.3390/nu12020327 · Cited by 32
Metadata
- Authors
- Borgelt, C.
- Venue
- WIREs Data Mining and Knowledge Discovery
- Publisher
- Wiley
- Published
- 2012-10-18
- Volume
- 2
- Issue
- 6
- Pages
- 437-456
- ISSN
- 1942-4787 (ISSN-L)
- 1942-4795
- OpenAlex
- W2129490689
- Cited by
- 291
- References
- 3
- FWCI
- 52.42
- Type
- journal article
- Language
- en
- Open access
- closed
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