RuleML 2010: Semantic Web Rules pp 273-288 | Cite as
An XML Format for Association Rule Models Based on the GUHA Method
Conference paper
Abstract
This paper proposes the GUHA AR Model, an XML Schema-based formalism for representing the setting and results of association rule (AR) mining tasks. In contrast to the item-based representation of the PMML 4.0 AssociationModel, the proposed expresses the association rule as a couple of general boolean attributes related by condition on one or more arbitrary interest measures. This makes the GUHA AR Model suitable also for other than apriori-based AR mining algorithms, such as those mining for disjunctive or negative ARs. In addition, there are practically important research results on special logical calculi formulas which correspond to such association rules. The GUHA AR Model is intended as a replacement of the PMML AssociationModel. It is tightly linked to the Background Knowledge Exchange Format (BKEF), an XML schema proposed for representation of data-mining related domain knowledge, and to the AR Data Mining Ontology ARON.
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References
- 1.Antonie, M.-L., Zaïane, O.R.: Mining positive and negative association rules: an approach for confined rules. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) PKDD 2004. LNCS (LNAI), vol. 3202, pp. 27–38. Springer, Heidelberg (2004)CrossRefGoogle Scholar
- 2.Berka, P., Rauch, J., Marie, T.: Data mining in atherosclerosis risk factor data. In: Data Mining and Medical Knowledge Management: Cases and Applications, vol. 15, pp. 376–397. IGI Global (2009)Google Scholar
- 3.Guazzelli, A., Lin, W.-C., Jena, T.: PMML in Action (2010)Google Scholar
- 4.Hahsler, M., Grün, B., Hornik, K.: Arules - a computational environment for mining association rules and frequent item sets. Journal of Statistical Software 14(15), 1–25, 9 (2005)CrossRefGoogle Scholar
- 5.Hájek, P. (ed.): International Journal of Man-Machine Studies, second special issue on GUHA 15 (1981)Google Scholar
- 6.Hájek, P., Havránek, T.: Mechanizing Hypothesis Formation. Springer, Heidelberg (1978)CrossRefMATHGoogle Scholar
- 7.Hájek, P., Holeňa, M., Rauch, J.: The GUHA method and its meaning for data mining. Journal of Computer and System Science, 34–38 (2010)Google Scholar
- 8.Hipp, J., Güntzer, U., Nakhaeizadeh, G.: Algorithms for association rule mining — a general survey and comparison. SIGKDD Explor. Newsl. 2(1), 58–64 (2000)CrossRefGoogle Scholar
- 9.Kliegr, T., Ovečka, M., Zemánek, J.: Topic maps for association rule mining. In: Proceedings of TMRA 2009. University of Leipzig (2009)Google Scholar
- 10.Kliegr, T., Ralbovský, M., Svátek, V., Šimůnek, M., Jirkovský, V., Nemrava, J., Zemánek, J.: Semantic analytical reports: A framework for post-processing data mining results. In: Rauch, J., Raś, Z.W., Berka, P., Elomaa, T. (eds.) ISMS 2009. LNCS, vol. 5722, pp. 453–458. Springer, Heidelberg (2009)CrossRefGoogle Scholar
- 11.Kliegr, T., Svátek, V., Šimůnek, M., Štastný, D., Hazucha, A.: XML schema and topic map ontology for background knowledge in data mining. In: The 2nd IRMLES ESWC Workshop (2010)Google Scholar
- 12.Lín, V., Rauch, J., Svátek, V.: Content-based retrieval of analytic reports. In: Rule Markup Languages for Business Rules on the Semantic Web, Sardinia 2002, pp. 219–224 (2002)Google Scholar
- 13.Martin, R., Tomáš, K.: Using disjunctions in association mining. In: Perner, P. (ed.) ICDM 2007. LNCS (LNAI), vol. 4597, pp. 339–351. Springer, Heidelberg (2007)Google Scholar
- 14.Nanavati, A.A., Chitrapura, K.P., Joshi, S., Krishnapuram, R.: Mining generalised disjunctive association rules. In: CIKM 2001, pp. 482–489. ACM, New York (2001)Google Scholar
- 15.Olaru, A., Marinica, C., Guillet, F.: Local mining of association rules with rule schemas. In: CIDM, pp. 118–124. IEEE, Los Alamitos (2009)Google Scholar
- 16.Rauch, J.: Logic of association rules. Applied Intelligence (22), 9–28 (2005)Google Scholar
- 17.Rauch, J.: Considerations on logical calculi for dealing with knowledge in data mining. In: Data Mining: Foundations and Practice, pp. 177–199. Springer, Heidelberg (2009)Google Scholar
- 18.Rauch, J.: Logical aspects of the measures of interestingness of association rules. In: Advances in Machine Learning II, pp. 175–203. Springer, Berlin (2010)CrossRefGoogle Scholar
- 19.Rauch, J., Šimůnek, M.: An alternative approach to mining association rules. Foundation of Data Mining and Knowledge Discovery 6, 211–231 (2005)Google Scholar
- 20.Šimůnek, M.: Academic KDD project LISp-Miner. In: Advances in Soft Computing - Intelligent Systems Design and Applications, pp. 263–272. Springer, Heidelberg (2003)Google Scholar
- 21.Wettschereck, D., Mueller, S.: Exchanging data mining models with the Predictive Model Markup Language. In: Proceedings of the ECML/PKDD 2001 Worksh. on Integr. of DM Decision Supp. and Meta-Learning, pp. 55–66 (2001)Google Scholar
- 22.Wu, T., Chen, Y., Han, J.: Association mining in large databases: A re-examination of its measures. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) PKDD 2007. LNCS (LNAI), vol. 4702, pp. 621–628. Springer, Heidelberg (2007)CrossRefGoogle Scholar
- 23.Zhang, Y., Zhang, L., Nie, G., Shi, Y.: A survey of interestingness measures for association rules. In: International Conference on Business Intelligence and Financial Engineering, pp. 460–463 (2009)Google Scholar
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