Abstract
Data mining queries are often submitted concurrently to the data mining system. The data mining system should take advantage of overlapping of the mined datasets. In this paper we focus on frequent itemset mining and we discuss and experimentally evaluate the implementation of the Common Counting method on top of the Apriori algorithm. The general idea of Common Counting is to reduce the number of times the common parts of the source datasets are scanned during the processing of the set of frequent pattern queries.
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. (2003). Evaluation of Common Counting Method for Concurrent Data Mining Queries. In: Kalinichenko, L., Manthey, R., Thalheim, B., Wloka, U. (eds) Advances in Databases and Information Systems. ADBIS 2003. Lecture Notes in Computer Science, vol 2798. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39403-7_8
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DOI: https://doi.org/10.1007/978-3-540-39403-7_8
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