A Study of Interestingness Measures for Knowledge Discovery in Databases—A Genetic Approach
Conference paper
First Online:
Abstract
One of the vital areas of attention in the field of knowledge discovery is to analyze the interestingness measures in rule discovery and to select the best one according to the situation. There is a wide variety of interestingness measures available in data mining literature and it is difficult for user to select appropriate measure in a particular application domain. The main contribution of the paper is to compare these interestingness measures on diverse datasets by using genetic algorithm and select the best one according to the situation.
Keywords
Knowledge discovery Interestingness measures Datasets Genetic algorithmReferences
- 1.Han, J.J., Kamber, M., Pei, J.: Data Mining, Concepts and Techniques, 3rd edn. Morgan Kaufmann, Burlington (2011)Google Scholar
- 2.Vashishtha, J., Kumar, D., Ratnoo, S.: Revisiting interestingness measures for knowledge discovery in databases. In: Second International Conference on Advanced Computing and Communication Technologie (ACCT), IEEE, pp. 72–78 (2012)Google Scholar
- 3.Garima, G., Vashishtha, J.: Interestingness measures in rule mining: a valuation. Int. J. Eng. Res. Appl. 4(7), 93–100 (2014). ISSN: 2248-9622Google Scholar
- 4.Carvalho, D.R., Freitas, A.A., Ebecken, N.F.F.: A critical review of rule surprisingness measures. In: Proceedings of Data Mining IV—International Conference on Data Mining, vol. 7 (2003)Google Scholar
- 5.Vashishtha, J., Kumar, D., Ratnoo, S.: An evolutionary approach to discover intra- and inter-class exceptions in databases. Int. J. Intell. Syst. Technol. Appl. 12, 283–300 (2013)Google Scholar
- 6.Geng, L., Hamilton, H.J.: Interestingness measures for data mining: a survey. ACM Comput. Surv. 38(3), 1–32 (2006)CrossRefGoogle Scholar
- 7.Triantaphyllou, E., Felici, G.: Data Mining and Knowledge Discovery Approaches Based on Rule Induction Techniques, vol. 6. Springer, Berlin (2006)Google Scholar
- 8.Freitas, A.A.: Data mining and Knowledge Discovery with Evolutionary Algorithms. Natural Computing Series. Springer, New York (2002)Google Scholar
- 9.Vashishtha, J., Kumar, D., Ratnoo, S., Kundu, K.: Mining comprehensible and interesting rules: a genetic algorithm approach. Int. J. Comput. Appl. 31(1), 39–47 (2011) (0975–8887)Google Scholar
- 10.Agrawal, R., Imielinski, T., Swami, A.: Mining associations between sets of items in large databases. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 207–216. Washington, DC. (1993)Google Scholar
- 11.Pagallo, G., Haussler, D.: Boolean feature discovery in empirical leaning. Mach. Learn. 5(1), 71–99 (1990)CrossRefGoogle Scholar
- 12.Smyth, P., Rodney, M.G.: Rule induction using information theory. In: Knowledge Discovery in Database, pp. 159–176. AAAI/MIT Press, Cambridge (1991)Google Scholar
- 13.Piatetsky-Shapiro, G.: Discovery, analysis, and presentation of strong rules. In: Piatetsky-Shapiro, G., Frawley, W.J. (eds.) Knowledge Discovery in Databases, pp. 229–248. MIT Press, Cambridge (1991)Google Scholar
- 14.Tan, P., Kumar, V., Srivastava, J.: Selecting the right interestingness measure for association patterns. In: Proceedings of the 8th International Conference on Knowledge Discovery and Data Mining (KDD 2002), pp. 32–41. Edmonton, Canada (2002)Google Scholar
- 15.Breiman, L., Freidman, J., Olshen, R., Stone, C.: Classification and Regression Trees. Wadsworth and Brooks, Pacific Grove (1984)MATHGoogle Scholar
- 16.Brin, S., Motwani, R., Ullman, J.D., Tsur, S.: Dynamic itemset counting and implication rules for market basket data. In: Proceedings of the ACM SIGMOD International Conference on Management of Data. ACM SIGMOD, pp. 265–276 (1997)Google Scholar
- 17.Quinlan, J.R.: Induction of decision trees. Mach. Learn. 1(1), 81–106 (1986)Google Scholar
Copyright information
© Springer India 2015