Abstract
One of the classic data mining problems is discovery of frequent item-sets. Frequent itemset discovery tasks can be regarded as advanced database queries specifying the source dataset, the minimum support threshold, and optional constraints on itemsets. We consider a data mining system which supports storing of results of previous queries in the form of materialized data mining views. Previous work on materialized data mining views addressed the issue of reusing results of one of the previous frequent itemset queries to efficiently answer the new query. In this paper we present a new approach to frequent itemset query processing in which a collection of materialized views can be used for that purpose.
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This work was partially supported by the grant no. 4T11C01923 from the State Committee for Scientific Research (KBN), Poland.
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References
Agrawal, R., Imielinski, T., Swami, A. (1993) Mining Association Rules Between Sets of Items in Large Databases. Proceedings of the 1993 ACM SIGMOD Conference on Management of Data, Washington, D. C., 207–216
Agrawal, R., Srikant, R. (1994) Fast Algorithms for Mining Association Rules. Proceedings of the 20th International Conference on Very Large Data Bases, Santiago de Chile, Chile, 487–499
Baralis, E., Psaila, G. (1999) Incremental Refinement of Mining Queries. Proceedings of the 1st International Conference on Data Warehousing and Knowledge Discovery (DaWaK), Florence, Italy, 173–182
Cheung, D. W.-L., Han, J., Ng, V., Wong, C. Y. (1996) Maintenance of discovered association rules in large databases: An incremental updating technique. Proceedings of the 12th International Conference on Data Engineering, New Orleans, Louisiana, USA, 106–114
Hettich, S., Bay, S. D. (1999) The UCI KDD Archive [http://kdd.ics.uci.edu]. Irvine, CA: University of California, Department of Information and Computer Science
Imielinski, T., Mannila, H. (1996) A Database Perspective on Knowledge Discovery. Communications of the ACM 39(11), 58–64
Morzy, T., Wojciechowski, M., Zakrzewicz, M. (2000) Materialized Data Mining Views. Proceedings of the 4th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD 2000), Lyon, France, 65–74
Nag, B., Deshpande, P. M., DeWitt, D. J. (1999) Using a Knowledge Cache for Interactive Discovery of Association Rules. Proceedings of the 5th International Conference on Knowledge Discovery and Data Mining, San Diego, California, 244–253
Roussopoulos, N. (1998) Materialized Views and Data Warehouses. SIGMOD Record, 27(1), 21–26
Zakrzewicz, M., Morzy, M., Wojciechowski, M. (2004) A Study on Answering a Data Mining Query Using a Materialized View. Proceedings of the 19th International Symposium on Computer and Information Sciences (ISCIS’04), Kemer — Antalya, Turkey, 493–502
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Wojciechowski, M., Zakrzewicz, M. (2005). Efficient Processing of Frequent Itemset Queries Using a Collection of Materialized Views. In: Kłopotek, M.A., Wierzchoń, S.T., Trojanowski, K. (eds) Intelligent Information Processing and Web Mining. Advances in Soft Computing, vol 31. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32392-9_16
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DOI: https://doi.org/10.1007/3-540-32392-9_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25056-2
Online ISBN: 978-3-540-32392-1
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