The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Clustering association rules
Abstract
The authors consider the problem of clustering two-dimensional association rules in large databases. They present a geometric-based algorithm, BitOp, for performing the clustering, embedded within an association rule clustering system, ARCS. Association rule clustering is useful when the user desires to segment the data. They measure the quality of the segmentation generated by ARCS using the minimum description length (MDL) principle of encoding the clusters on several databases including noise and errors. Scale-up experiments show that ARCS, using the BitOp algorithm, scales linearly with the amount of data.
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Cites
Quinlan, J. R. (1986) · Machine Learning · doi:10.1007/bf00116251 · Cited by 21,439
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
Self-citationFisher, R. A. (1936) · BMJ · doi:10.1136/bmj.1.3923.554-a · Cited by 4,980
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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
Frequent pattern mining: current status and future directions
Han, J., Cheng, H., Xin, D., & Yan, X. (2007) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-006-0059-1 · Cited by 1,380
(2003) · Cited by 165
Constrained frequent pattern mining: a pattern-growth view
Pei, J., & Han, J. (2002) · ACM SIGKDD Explorations Newsletter · doi:10.1145/568574.568580 · Cited by 133
Metadata
- Authors
- Lent, B., Swami, A., & Widom, J.
- Venue
- Proceedings 13th International Conference on Data Engineering
- Publisher
- IEEE Comput. Soc. Press
- Published
- 2002-11-22
- Pages
- 220-231
- ISSN
- 1084-4627 (ISSN-L)
- OpenAlex
- W2097800052
- Cited by
- 374
- References
- 3
- FWCI
- 23.22
- Type
- conference paper
- Language
- en
- Open access
- closed
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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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