Abstract
Non-redundant association rule mining requires generation of both closed itemsets and their minimal generators. However, only a few researchers have addressed both the issues for data streams. Further, association rule mining is now considered as multiobjective problem where multiple measures like correlation coefficient, recall, comprehensibility, lift etc can be used for evaluating a rule. Discovery of multiobjective association rules in data streams has not been paid much attention.
In this paper, we have proposed a 3-step algorithm for generation of multiobjective non-redundant association rules in data streams. In the first step, an online procedure generates closed itemsets incrementally using state of the art CLICI algorithm and stores the results in a lattice based synopsis. An offline component invokes the proposed genMG and genMAR procedures whenever required. Without generating candidates, genMG computes minimal generators of all closed itemsets stored in the synopsis. Next, genMAR generates multiobjective association rules using non-dominating sorting based on user specified interestingness measures that are computed using the synopsis. Experimental evaluation using synthetic and real life datasets demonstrates the efficiency and scalability of the proposed algorithm.
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References
Agarwal, R., Srikant, R.: Fast Algorithms for Mining Association Rules. In: 20th International Conference on Very Large Databases, pp. 487–499 (1994)
Chang, J., Lee, W.: Finding Recent Frequent Itemsets Adaptively over Online Data stream. In: 9th ACM SIGKDD, pp. 487–492. ACM Press, New York (2003)
Cheng, J., Ke, Y., Ng, W.: A Survey on Algorithms for Mining Frequent Itemsets over Data stream. KAIS Journal 16(1), 1–27 (2008)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. IEEE Transaction on Evolutionary Computation 6(2), 181–197 (2002)
Geng, L., Hamilton, H.J.: Interestingness Measures for Data Mining: A Survey. ACM Computing Surveys, 38(3), Article 9 (2006)
Gupta, A., Bhatnagar, V., Kumar, N.: Mining Closed Itemsets in Data Stream Using Formal Concept Analysis. In: Bach Pedersen, T., Mohania, M.K., Tjoa, A.M. (eds.) DAWAK 2010. LNCS, vol. 6263, pp. 285–296. Springer, Heidelberg (2010)
Han, J., Cheng, H., Xin, D., Yan, X.: Frequent Pattern Mining: Current Status and Future Directions. Journal of DMKD 15, 55–86 (2007)
Heravi, M.J., Zaiane, O.R.: A Study on Interestingness Measures for Associative Classifiers. In: ACM Symposium on Applied Computing (2010)
Ishibuchi, H., Kuwajima, I., Nojima, Y.: Multiobjective Association Rule Mining. In: PPSN Workshop on Multiobjective Problem Solving from Nature (2006)
Jiang, N., Gruenwald, L.: CFI-Stream: Mining Closed Frequent Itemsets in Data stream. In: ACM SIGKDD, Poster Paper, pp. 592–597. ACM Press, New York (2006)
Jiang, N., Gruenwald, L.: Estimating Missing Data in Data Streams. In: International Conference on Database Systems for Advanced Applications, pp. 981–987 (2007)
Li, H., Ho, C., Lee, S.: Incremental Updates of Closed Frequent Itemsets Over Continuous Data stream. Expert Systems with Applications 36, 2451–2458 (2009)
Pasquier, N., et al.: Efficient Mining of Association Rules using Closed Itemset Lattices. Journal of Information Systems 24(1), 25–46 (1999)
Shin, S.J., Lee, W.S.: An On-line Interactive Method for Finding Association Rules Data Streams. ACM CIKM (2007)
Stumme, G., et al.: Computing Iceberg Concept Lattices with Titanic. Journal on Knowledge and Data Engineering 42(2), 189–222 (2002)
Szathmary, L., Valtchev, P., Napoli, A., Godin, R.: Efficient Vertical Mining of Frequent Closures and Generators. In: Adams, N.M., Robardet, C., Siebes, A., Boulicaut, J.-F. (eds.) IDA 2009. LNCS, vol. 5772, pp. 393–404. Springer, Heidelberg (2009)
Tan, J., Bu, Y., Zhao, H.: Incremental Maintenance of Association Rules Over data Streams. In: International Conference on Networking and Digital Society (2010)
Vo, B., Le, B.: Fast algorithm for mining Minimal generators of FCI and their applications. In: IEEE International Conference on Computers and Industrial Engineering, pp. 1407–1411 (2009)
Chi, Y., Wang, H., Yu, P.S., Muntz, R.R.: Catch the Moment: Maintaining Closed Frequent Itemsets over a Stream Sliding Window. Journal of Knowledge and Information Systems 10, 265–294 (2006)
Zaki, M.J.: Generating Non-Redundant Association Rules. In: 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 34–43. ACM Press, New York (2000)
Zaki, M.J.: Mining Non-Redundant Association Rules. In: Data Mining and Knowledge Discovery, vol. 9, pp. 223–248 (2004)
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Gupta, A., Kumar, N., Bhatnagar, V. (2012). Mining of Multiobjective Non-redundant Association Rules in Data Streams. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2012. Lecture Notes in Computer Science(), vol 7268. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29350-4_9
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DOI: https://doi.org/10.1007/978-3-642-29350-4_9
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