The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
Algorithms for association rule mining — a general survey and comparison
Open accessAbstract
Today there are several efficient algorithms that cope with the popular and computationally expensive task of association rule mining.Actually, these algorithms are more or less described on their own.In this paper we explain the fundamentals of association rule mining and moreover derive a general framework.Based on this we describe today's approaches in context by pointing out common aspects and differences.After that we thoroughly investigate their strengths and weaknesses and carry out several runtime experiments.It turns out that the runtime behavior of the algorithms is much more similar as to be expected.
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Citations
Cites
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
Fast algorithms for mining association rules
Agrawal, R., & Srikant, R. (1998) · Cited by 11,307
Fast Algorithms for Mining Association Rules in Large Databases
Agrawal, R., & Srikant, R. (1994) · Very Large Data Bases · Cited by 9,419
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
Agrawal, R., & Srikant, R. (2002) · Proceedings of the Eleventh International Conference on Data Engineering · doi:10.1109/icde.1995.380415 · Cited by 5,155
Mining frequent patterns without candidate generation
Han, J., Pei, J., & Yin, Y. (2000) · Proceedings of the 2000 ACM SIGMOD international conference on Management of data · doi:10.1145/342009.335372 · Cited by 3,216
Dynamic itemset counting and implication rules for market basket data
Brin, S., Motwani, R., Ullman, J. D., & Tsur, S. (1997) · Proceedings of the 1997 ACM SIGMOD international conference on Management of data - SIGMOD '97 · doi:10.1145/253260.253325 · Cited by 1,951
Mining generalized association rules
Srikant, R., & Agrawal, R. (1997) · Future Generation Computer Systems · doi:10.1016/s0167-739x(97)00019-8 · Cited by 1,618
An Efficient Algorithm for Mining Association Rules in Large Databases
Savasere, A., Omiecinski, E. R., & Navathe, S. B. (1995) · Cited by 1,590
Mining quantitative association rules in large relational tables
Srikant, R., & Agrawal, R. (1996) · Proceedings of the 1996 ACM SIGMOD international conference on Management of data - SIGMOD '96 · doi:10.1145/233269.233311 · Cited by 1,480
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Biclustering algorithms for biological data analysis: a survey
Madeira, S. C., & Oliveira, A. L. (2004) · IEEE/ACM Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2004.2 · Cited by 2,049
Knowledge graph refinement: A survey of approaches and evaluation methods
Paulheim, H. (2016) · Semantic Web · doi:10.3233/sw-160218 · Cited by 849
Data mining in manufacturing: a review based on the kind of knowledge
Choudhary, A. K., Harding, J. A., & Tiwari, M. K. (2009) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-008-0145-x · Cited by 494
Business intelligence: data mining and optimization for decision making
Vercellis, C., Ahmadi, A., & Mohebi, A. (2016) · doi:10.5281/zenodo.13983826 · Cited by 398
Human Activity Recognition Data Analysis: History, Evolutions, and New Trends
Ariza-Colpas, P. P., Vicario, E., Oviedo-Carrascal, A. I., Butt Aziz, S., Piñeres-Melo, M. A., Quintero-Linero, A., & Patara, F. (2022) · Sensors · doi:10.3390/s22093401 · Cited by 52
Metadata
- Authors
- Hipp, J., Güntzer, U., & Nakhaeizadeh, G.
- Venue
- ACM SIGKDD Explorations Newsletter
- Published
- 2000-06
- Volume
- 2
- Issue
- 1
- Pages
- 58-64
- ISSN
- 1931-0145 (ISSN-L)
- 1931-0153
- OpenAlex
- W2045487373
- Cited by
- 962
- References
- 28
- FWCI
- 67.75
- Type
- journal article
- Language
- en
- Open access
- bronze
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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