The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Finding interesting rules from large sets of discovered association rules
Abstract
Association rules, introduced by Agrawal, Imielinski, and Swami, are rules of the form "for 90 % of the rows of the relation, if the row has value 1 in the columns in set W , then it has 1 also in column B". Efficient methods exist for discovering association rules from large collections of data. The number of discovered rules can, however, be so large that browsing the rule set and finding interesting rules from it can be quite difficult for the user. We show how a simple formalism of rule templates makes it possible to easily describe the structure of interesting rules. We also give examples of visualization of rules, and show how a visualization tool interfaces with rule templates. 1 Introduction Data mining (knowledge discovery in databases) is a field of increasing interest combining databases, artificial intelligence, and machine learning. The purpose of data mining is to facilitate understanding large amounts of data by discovering interesting regularities or exceptions (see e...
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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
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Metadata
- Authors
- Klemettinen, M., Mannila, H., Ronkainen, P., Toivonen, H., & Verkamo, A. I.
- Venue
- Proceedings of the third international conference on Information and knowledge management - CIKM '94
- Publisher
- ACM Press
- Published
- 1994
- Pages
- 401-407
- OpenAlex
- W1978387271
- Cited by
- 712
- References
- 1
- FWCI
- 17.35
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Klemettinen, M., Mannila, H., Ronkainen, P., Toivonen, H., & Verkamo, A. I. (1994). Finding interesting rules from large sets of discovered association rules. Proceedings of the third international conference on Information and knowledge management - CIKM '94. https://doi.org/10.1145/191246.191314
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
