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.
This work was partially supported by the grant no. 4T11C01923 from the State Committee for Scientific Research (KBN), Poland.
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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
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