Abstract
We propose a family of large itemset counting algorithms which adapt to the amount of main memory available. By using historical or sampling data, the potential large itemsets (candidates) and the false candidates are identified earlier. Redundant computation is reduced(thus overall CPU time reduced) by counting different sizes of candidates together and the use of a dynamic trie. By counting candidates earlier and counting more candidates in each scan, the algorithms reduce the overall number of scans required.
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References
Rakesh Agrawal, Tomasz Imielinski, and Arun N. Swami. Mining association rules between sets of items in large databases. In Peter Buneman and Sushil Jajodia, editors, Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, pages 207–216, Washington, D.C., 26–28 May 1993.
Rakesh Agrawal, Heikki Mannila, Ramakrishnan Srikant, Hannu Toivonen, and A. Inkeri Verkamo. Fast discovery of association rules. In Usama M. Fayyad, Gregory Piatetsky-Shapiro, Padhraic Smyth, and Ramasamy Uthurusamy, editors, Advances in Knowledge Discovery and Data Mining, pages 307–328, Menlo Park, CA, 1996. AAAI Press.
Rakesh Agrawal and John C. Shafer. Parallel mining of association rules. IEEE Transactions on Knowledge and Data Engineering, 8(6):962–969, December 1996.
Rakesh Agrawal and Ramakrishnan Srikant. Fast Algorithms for Mining Association Rules in Large Databases. In Proceedings of the Twentieth International Conference on Very Large Databases, pages 487–499, Santiago, Chile, 1994.
Sergey Brin, Rajeev Motwanik, Jeffrey D. Ullman, and Shalom Tsur. Dynamic itemset counting and implication rules for market basket data. In Proceedings of the ACM SIGMOD Conference, pages 255–264, 1997.
Eui-Hong Han, George Karypis, and Vipin Kumar. Scalable parallel data mining for association rules. In Proceedings of the ACM SIGMOD Conference, pages 277–288, 1997.
Jun-Lin Lin and M. H. Dunham. Mining association rules: Anti-skew algorithms. In Proceedings of the 14th International Conference on Data Engineering, Olando, Florida, February 1998. IEEE Computer Society Press.
Heikki Mannila, Hannu Toivonen, and A. Inkeri Verkamo. Efficient algorithms for discovering association rules. In Usama M. Fayyad and Ramasamy Uthurusamy,editors, AAAI Workshop on Knowledge Discovery in Databases (KDD-94), pages 181–192, July 1994.
Ashoka Savasere, Edward Omiecinski, and Shamkant B. Navathe. An efficient algorithm for mining association rules in large databases. In Proceedings of the 21nd International Conference on Very Large Databases, pages 432–444, Zurich, Swizerland, 1995.
Takahiko Shintani and Masaru Kitsuregawa. Hash based parallel algorithms for mining association rules. In Proceedings of PDIS, 1996.
Hannu Toivonen. Sampling large databases for association rules. In Proceedings of the 22nd International Conference on Very Large Databases, pages 134–145, Mumbai, India, 1996.
Yongqiao Xiao and Margaret H Dunham. Considering main memory in mining association rules. In Technical Report-99-CSE-4, Department of Computer Science, Southern Methodist University, 1999.
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Xiao, Y., Dunham, M.H. (1999). Considering Main Memory in Mining Association Rules. In: Mohania, M., Tjoa, A.M. (eds) DataWarehousing and Knowledge Discovery. DaWaK 1999. Lecture Notes in Computer Science, vol 1676. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48298-9_23
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DOI: https://doi.org/10.1007/3-540-48298-9_23
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