Abstract
The association rule mining is now widely used in many fields such as commerce, telecom, insurance, and bioinformatics. Though it is improved in performance, the real commerce database size and dimension has greatly increased to a point of creating thousands or millions of association rules. In spite of using minimum support and confidence thresholds to help weed out or exclude the exploration of uninteresting rules, many rules that are not interesting to the user may still be produced. We develop intelligent data mining technique that generate and evaluate association rules by hybrid interestingness measures based common sense knowledge. We provide new and interesting knowledge to users by Common-Sense Measures. We define a Common-Sense Measures by similarity between association rules and common sense knowledge. This measure is based on the common sense knowledge network.
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References
Jiawei, H., Micheline, K.: DATA MINING-Concept and Technique. Morgan Kaufmann (2006)
McGarry, K.: A Survey of Interestingness Measures for Knowledge Discovery. The Knowledge Engineering Review, 1–24 (2005)
Liqiang, G., Hamilton, J.: Interestingness Measures for Data Mining: A Servey. ACM Computing Surveys 38 (September 2006)
The ConceptNet Project V2.1, http://web.media.mit.edu/~hugo/conceptnet
Liu, H., Singh, P.: ConceptNet: A Practical Commonsense Reasoning Toolkit. BT Technology Journal 22 (2004)
Yanchang, Z.: Post-Mining of Association Rules. Information Science Reference (2009)
Wu, T., Chen, Y., Han, J.: Re-examination of interestingness measures in pattern mining: a unified framework. Data Mining and Knowledge 21(3), 371–397 (2010)
David, J., Guillet, F., Gras, R., Briand, H.: Comparison of interestingness measures applied to textual taxonomies matching. Revue des Nouvelles Technologies de l’Information (2008)
Suzuki, E.: Compression-Based Measures for Mining Interesting Rules. In: Chien, B.-C., Hong, T.-P., Chen, S.-M., Ali, M. (eds.) IEA/AIE 2009. LNCS, vol. 5579, pp. 741–746. Springer, Heidelberg (2009)
Sahar, S.: On incorporating subjective interestingness into the mining process. In: Proceedings of the 2002 IEEE ICDM 2002, Maebashi City, Japan, pp. 681–684 (2002)
Jorge, A., Pocas, J.: A post-processing environment for browsing large sets of association rules. In: Proceedings of Second International Workshop on Integration and Collaboration Aspects of Data Mining. Springer (2002)
Liu, B., Chen, W.: Analyzing the subjective interestingness of association rules. IEEE Intelligent System and Their Applications 15(5), 47–55 (2000)
Techapichetvanich, K., Datta, A.: VisAR: A New Technique for Visualizing Mined Association Rules. In: Li, X., Wang, S., Dong, Z.Y. (eds.) ADMA 2005. LNCS (LNAI), vol. 3584, pp. 88–95. Springer, Heidelberg (2005)
Havasi, C., Speer, R., Alonso, J.: ConceptNet 3: a Flexible, Multilingual Semantic Network for Common Sense Knowledge. In: Proceedings of Recent Advances in Natural Languges Processing (2007)
Baik, J., Kim, S., Lee, S.: Automatic Construction of Alternative Word Candidates to Improve Patent Information Search Quality. Journal of KIISE, Software and Applications 36(10), 777–875 (2009)
Palmer, M., Wu, Z.: Verb semantics and lexical selection. In: Proceedings of the 32nd Annual Meeting of the Association for Computational Linguistics, Las Cruces, New Mexico (1994)
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Lee, I., Yong, HS. (2012). Common Sense Knowledge Based Hybrid Interestingness Measures for Data Mining. In: Lee, G., Howard, D., Kang, J.J., Ślęzak, D. (eds) Convergence and Hybrid Information Technology. ICHIT 2012. Lecture Notes in Computer Science, vol 7425. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32645-5_19
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DOI: https://doi.org/10.1007/978-3-642-32645-5_19
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