Abstract
Mining weighted based association rules has received a great attention and consider as one of the important area in data mining. Most of the items in transactional databases are not always carried with the same binary value. Some of them might associate with different level of important such as the profit margins, weights, etc. However, the study in this area is quite complex and thus required an appropriate scheme for rules detection. Therefore, this paper proposes a new measure called Weighted Support Association Rules (WSAR*) measure to discover the significant association rules and Weighted Least Association Rules (WELAR) framework. Experiment results shows that the significant association rules are successfully mined and the unimportant rules are easily differentiated. Our algorithm in WELAR framework also outperforms the benchmarked FP-Growth algorithm.
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References
Agrawal, R., Imielinski, T., Swami, A.: Database Mining: A Performance Perspective. IEEE Transactions on Knowledge and Data Engineering 5(6), 914–925 (1993)
Lan, G.-C., Hong, T.-P., Tseng, V.S.: A Novel Algorithm for Mining Rare-Utility Itemsets in a Multi-Database Environment. In: The 26th Workshop on Combinatorial Mathematics and Computation Theory, pp. 293–302 (2009)
Weiss, G.M.: Mining with Rarity: a Unifying Framework. SIGKDD Explorations Newsletter 6(1), 7–19 (2004)
Abdullah, Z., Herawan, T., Deris, M.M.: Mining Significant Least Association Rules Using Fast SLP-Growth Algorithm. In: Kim, T.-h., Adeli, H. (eds.) AST/UCMA/ISA/ACN 2010. LNCS, vol. 6059, pp. 324–336. Springer, Heidelberg (2010)
Kiran, R.U., Reddy, P.K.: An Improved Multiple Minimum Support Based Approach to Mine Rare Association Rules. In: Proceeding of IEEE Symposium on Computational Intelligence and Data Mining, pp. 340–347 (2009)
Zhou, L., Yau, S.: Assocation Rule and Quantative Association Rule Mining among Infrequent Items. In: Proceeding of ACM SIGKDD, Article No. 9 (2007)
Koh, Y.S., Rountree, N.: Finding Sporadic Rules Using Apriori-Inverse. In: Ho, T.-B., Cheung, D., Liu, H. (eds.) PAKDD 2005. LNCS (LNAI), vol. 3518, pp. 97–106. Springer, Heidelberg (2005)
Yun, H., Ha, D., Hwang, B., Ryu, K.H.: Mining Association Rules on Significant Rare Data using Relative Support. The Journal of Systems and Software 67(3), 181–190 (2003)
Liu, B., Hsu, W., Ma, Y.: Mining Association Rules with Multiple Minimum Supports. In: Proceeding of ACM SIGKDD 1999, pp. 337–341 (1999)
Wang, K., Hee, Y., Han, J.: Pushing Support Constraints into Association Rules Mining. IEEE Transactions on Knowledge and Data Engineering 15(3), 642–658 (2003)
Tao, F., Murtagh, F., Farid, M.: Weighted Association Rule Mining using Weighted Support and Significant Framework. In: Proceeding of ACM SIGKDD 2003, pp. 661–666 (2003)
Cai, C.H., Fu, A.W.C., Cheng, C.H., Kwong, W.W.: Mining Association Rules with Weighted Items. In: Proceedings of the international Database Engineering and Application Symposium, Cardiff, UK, pp. 68–77 (1998)
Ding, J.: Efficient Association Rule Mining among Infrequent Items. Ph.D. Thesis, University of Illinois at Chicago (2005)
Abdullah, Z., Herawan, T., Deris, M.M.: Scalable Model for Mining Critical Least Association Rules. In: Zhu, R., Zhang, Y., Liu, B., Liu, C. (eds.) ICICA 2010. LNCS, vol. 6377, pp. 509–516. Springer, Heidelberg (2010)
Park, J.S., Chen, M.-S., Yu, P.S.: An Effective Hash based Algorithm for Mining Association Rules. In: Carey, M.J., Schneider, D.A. (eds.) Proceedings of the 1995 ACM SIG-MOD International Conference on Management of Data, San Jose, California, pp. 175–186 (1995)
Selvi, C.S.K., Tamilarasi, A.: Mining association rules with dynamic and collective support thresholds. International Journal on Open Problems Computational Mathematics 2(3), 427–438 (2009)
Han, J., Pei, H., Yin, Y.: Mining Frequent Patterns without Candidate Generation. In: Proceeding of the 2000 ACM SIGMOD, pp. 1–12 (2000)
Szathmary, L.: Generating Rare Association Rules Using the Minimal Rare Itemsets Family. International Journal of Software and Informatics 4(3), 219–238 (2010)
Khan, M.S., Muyeba, M., Coenen, F.: Weighted Association Rule Mining from Binary and Fuzzy Data. In: Proceedings of the 8th industrial conference on Advances in Data Mining: Medical Applications, E-Commerce, Marketing, and Theoretical Aspects, Leipzig, Germany, July 16-18, vol. 212, pp. 200–212 (2008)
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Abdullah, Z., Herawan, T., Mat Deris, M. (2011). An Alternative Measure for Mining Weighted Least Association Rule and Its Framework. In: Zain, J.M., Wan Mohd, W.M.b., El-Qawasmeh, E. (eds) Software Engineering and Computer Systems. ICSECS 2011. Communications in Computer and Information Science, vol 180. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22191-0_42
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DOI: https://doi.org/10.1007/978-3-642-22191-0_42
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