Abstract
Frequent itemset mining can be regarded as advanced data-base querying where a user specifies the dataset to be mined and constraints to be satisfied by the discovered itemsets. One of the research directions influenced by the above observation is the processing of sets of frequent itemset queries operating on overlapping datasets. Several methods of solving this problem have been proposed, all of them assuming selective access to the partitions of data determined by the overlapping of queries, and tested so far only on flat files. In this paper we theoretically and experimentally analyze the influence of data access paths available in database systems on the methods of frequent itemset query set processing, which is crucial from the point of view of their possible applications.
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References
Agrawal, R., Imielinski, T., Swami, A.N.: Mining association rules between sets of items in large databases. In: Buneman, P., Jajodia, S. (eds.) Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, pp. 207–216. ACM Press, New York (1993)
Agrawal, R., Mehta, M., Shafer, J.C., Srikant, R., Arning, A., Bollinger, T.: The quest data mining system. In: Simoudis, E., Han, J., Fayyad, U.M. (eds.) Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, pp. 244–249. AAAI Press, Menlo Park (1996)
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Bocca, J.B., Jarke, M., Zaniolo, C. (eds.) Proceedings of the 20th Int. Conf. on Very Large Data Bases, pp. 487–499. Morgan Kaufmann, San Francisco (1994)
Blockeel, H., Dehaspe, L., Demoen, B., Janssens, G., Ramon, J., Vandecasteele, H.: Improving the efficiency of inductive logic programming through the use of query packs. Journal of Artificial Intelligence Research 16, 135–166 (2002)
Grudzinski, P., Wojciechowski, M.: Integration of candidate hash trees in concurrent processing of frequent itemset queries using apriori. In: Proceedings of the 3rd ADBIS Workshop on Data Mining and Knowledge Discovery, pp. 71–81 (2007)
Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: Chen, W., Naughton, J.F., Bernstein, P.A. (eds.) Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, pp. 1–12. ACM, New York (2000)
Imielinski, T., Mannila, H.: A database perspective on knowledge discovery. Communications of the ACM 39(11), 58–64 (1996)
Jin, R., Sinha, K., Agrawal, G.: Simultaneous optimization of complex mining tasks with a knowledgeable cache. In: Grossman, R., Bayardo, R.J., Bennett, K.P. (eds.) Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 600–605. ACM, New York (2005)
Sellis, T.K.: Multiple-query optimization. ACM Transactions on Database Systems 13(1), 23–52 (1988)
Wojciechowski, M., Zakrzewicz, M.: Methods for batch processing of data mining queries. In: Proceedings of the 5th International Baltic Conference on Databases and Information Systems, pp. 225–236 (2002)
Wojciechowski, M., Galecki, K., Gawronek, K.: Three strategies for concurrent processing of frequent itemset queries using FP-growth. In: Džeroski, S., Struyf, J. (eds.) KDID 2006. LNCS, vol. 4747, pp. 240–258. Springer, Heidelberg (2007)
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Jedrzejczak, P., Wojciechowski, M. (2011). Data Access Paths in Processing of Sets of Frequent Itemset Queries. In: Kryszkiewicz, M., Rybinski, H., Skowron, A., Raś, Z.W. (eds) Foundations of Intelligent Systems. ISMIS 2011. Lecture Notes in Computer Science(), vol 6804. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21916-0_41
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DOI: https://doi.org/10.1007/978-3-642-21916-0_41
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