Abstract
Mining for association rules between items in a large database of sales transactions is an important database mining problem. However, the algorithms previously reported in the literature apply only to static databases. That is, when more transactions are added, the mining process must start all over again, without taking advantage of the previous execution and results of the mining algorithm. In this paper we present an efficient algorithm for mining association rules within the context of a dynamic database, (i.e., a database where transactions can be added). It is an extension of our Partition algorithm which was shown to reduce the I/O overhead significantly as well as to lower the CPU overhead for most cases when compared with the performance of one of the best existing association mining algorithms.
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R. Agrawal, T. Imielinski, and A. Swami. Mining association rules between sets of items in large databases. In Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, pages 207–216, Washington, DC, May 26–28 1993.
R. Agrawal and R. Srikant. Fast algorithms for mining association rules in large databases. In Proceedings of the 20th International Conference on Very Large Data Bases, Santiago, Chile, August 29–September 1 1994.
J. Han and Y. Fu. Discovery of multiple-level association rules from large databases. In Proceedings of the VLDB Conference, pages 420–431, September 1995.
M. Houtsma and A. Swami. Set-oriented mining of association rules. In Proceedings of the International Conference on Data Engineering, Taipei, Taiwan, March 1995.
J. S. Park, M-S. Chen, and P. S. Yu. An effective hash based algorithm for mining association rules. In Proceedings of the ACM-SIGMOD Conference on Management of Data, pages 229–248, San Jose, California, May 1995.
A. Savasere, E. Omiecinski, and S. Navathe. An efficient algorithm for mining association rules in large databases. In Proceedings of the 21st International Conference on Very Large Data Bases, pages 688–192, Zurich, Swizerland, August 1995.
A. Savasere, E. Omiecinski, and S. Navathe. An efficient algorithm for mining association rules in large databases. Technical Report GIT-CC-95-04, Georgia Institute of Technology, Atlanta, GA 30332, January 1995.
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© 1998 Springer-Verlag Berlin Heidelberg
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Omiecinski, E., Savasere, A. (1998). Efficient mining of association rules in large dynamic databases. In: Embury, S.M., Fiddian, N.J., Gray, W.A., Jones, A.C. (eds) Advances in Databases. BNCOD 1998. Lecture Notes in Computer Science, vol 1405. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0053471
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DOI: https://doi.org/10.1007/BFb0053471
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