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What makes patterns interesting in knowledge discovery systems
Abstract
One of the central problems in the field of knowledge discovery is the development of good measures of interestingness of discovered patterns. Such measures of interestingness are divided into objective measures-those that depend only on the structure of a pattern and the underlying data used in the discovery process, and the subjective measures-those that also depend on the class of users who examine the pattern. The focus of the paper is on studying subjective measures of interestingness. These measures are classified into actionable and unexpected, and the relationship between them is examined. The unexpected measure of interestingness is defined in terms of the belief system that the user has. Interestingness of a pattern is expressed in terms of how it affects the belief system. The paper also discusses how this unexpected measure of interestingness can be used in the discovery process.
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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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Discovering relative motion patterns in groups of moving point objects
Laube, P., Imfeld, S., & Weibel, R. (2005) · International Journal of Geographical Information Science · doi:10.1080/13658810500105572 · Cited by 275
A comprehensive survey of numeric and symbolic outlier mining techniques
Agyemang, M., Barker, K., & Alhajj, R. (2006) · Intelligent Data Analysis · doi:10.3233/ida-2006-10604 · Cited by 164
RuleSelector: Selecting Conditional Action Rules from User Behavior Patterns
Srinivasan, V., Koehler, C., & Jin, H. (2018) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3191767 · Cited by 23
(2010) · FedUni ResearchOnline (Federation University Australia) · Cited by 9
Metadata
- Authors
- Silberschatz, A., & Tuzhilin, A.
- Venue
- IEEE Transactions on Knowledge and Data Engineering
- Published
- 1996
- Volume
- 8
- Issue
- 6
- Pages
- 970-974
- ISSN
- 1041-4347 (ISSN-L)
- OpenAlex
- W2136663836
- Cited by
- 679
- References
- 1
- FWCI
- 49.14
- Type
- journal article
- Language
- en
- Open access
- closed
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- Silberschatz, A., & Tuzhilin, A. (1996). What makes patterns interesting in knowledge discovery systems. IEEE Transactions on Knowledge and Data Engineering. https://doi.org/10.1109/69.553165
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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.
