ISMIS 2011: Foundations of Intelligent Systems pp 113-122 | Cite as
Applying Domain Knowledge in Association Rules Mining Process – First Experience
Conference paper
Abstract
First experiences with utilization of formalized items of domain knowledge in a process of association rules mining are described. We use association rules - atomic consequences of items of domain knowledge and suitable deduction rules to filter out uninteresting association rules. The approach is experimentally implemented in the LISp–Miner system.
Keywords
Association Rule Data Matrix Domain Knowledge Domain Expert Association Rule Mining
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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References
- 1.Hájek, P., Havránek, T.: Mechanising Hypothesis Formation - Mathematical Foundations for a General Theory. Springer, Heidelberg (1978)CrossRefMATHGoogle Scholar
- 2.Kliegr, T., et al.: Semantic Analytical Reports: A Framework for Post processing data Mining Results. In: Rauch, J., et al. (eds.) Foundations of Intelligent Systems, pp. 88–98. Springer, Heidelberg (2009)CrossRefGoogle Scholar
- 3.Qiang, Y., Xindong, W.: 10 Challenging Problems in Data Mining Research. International Journal of Information Technology & Decision Making 5(4), 597–604 (2006)CrossRefGoogle Scholar
- 4.Rauch, J.: Logic of Association Rules. Applied Intelligence 22, 9–28 (2005)CrossRefMATHGoogle Scholar
- 5.Rauch, J.: Considerations on Logical Calculi for Dealing with Knowledge in Data Mining. In: Ras, Z.W., Dardzinska, A. (eds.) Advances in Data Management. Studies in Computational Intelligence, vol. 223, pp. 177–199. Springer, Heidelberg (2009)CrossRefGoogle Scholar
- 6.Rauch, J., Šimůnek, M.: An Alternative Approach to Mining Association Rules. In: Lin, T.Y., et al. (eds.) Data Mining: Foundations, Methods, and Applications, pp. 219–238. Springer, Heidelberg (2005)Google Scholar
- 7.Rauch, J., Šimůnek, M.: Dealing with Background Knowledge in the SEWEBAR Project. In: Berendt, B., Mladenič, D., de Gemmis, M., Semeraro, G., Spiliopoulou, M., Stumme, G., Svátek, V., Železný, F., et al. (eds.) Knowledge Discovery Enhanced with Semantic and Social Information. Studies in Computational Intelligence, vol. 220, pp. 89–106. Springer, Heidelberg (2009)CrossRefGoogle Scholar
- 8.Suzuki, E.: Discovering interesting exception rules with rule pair. In: Fuernkranz, J. (ed.) Proceedings of the ECML/PKDD Workshop on Advances in Inductive Rule Learning, pp. 163–178 (2004)Google Scholar
- 9.Šimůnek, M., Tammisto, T.: Distributed Data-Mining in the LISp-Miner System Using Techila Grid. In: Zavoral, F., Yaghob, J., Pichappan, P., El-Qawasmeh, E. (eds.) NDT 2010. Communications in Computer and Information Science, vol. 87, pp. 15–20. Springer, Heidelberg (2010)CrossRefGoogle Scholar
- 10.Šimůnek, M., Rauch, J.: EverMiner – Towards Fully Automated KDD Process, accepted for publication in Data Mining. In: TECH (2011) ISBN: 978-953-7619-X-XGoogle Scholar
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