Abstract
This paper presents new algorithms for the extraction of association rules from binary databases. Most existing methods operate by generating “candidate” sets, representing combinations of attributes which may be associated, and then testing the database to establish the degree of association. This may involve multiple database passes, and is also likely to encounter problems when dealing with “dense” data due to the increase in the number of sets under consideration. Our methods uses a single pass of the database to perform a partial computation of support for all sets encountered in the database, storing this in the form of a set enumeration tree. We describe algorithms for generating this tree and for using it to generate association rules.
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References
Agrawal, R. Imielinski, T. Swami, A. Mining Association Rules Between Sets of Items in Larg e Databases. SIGMOD-93, 207–216. May 1993.
Agrawal, R. and Srikant, R. Fast Algorithms for Mining Association Rules. Proc 20th VLDB Conference, Santiago, 487–499. 1994
Houtsma, M. and Swami, A. Set-oriented mining of association rules. Research Report RJ 9567, IBM Almaden Research Centre, San Jose, October 1993.
Zaki, M.J., Parthasarathy, S. Ogihara, M. and Li, W. New Algorithms for fast discovery of association rules. Technical report 651, University of Rochester, Computer Science Department, New York. July 1997.
Bayardo, R.J. Efficiently mining long patterns from databases. Proc ACM- SIGMOD Int Conf on Management of Data, 85–93, 1998.
Savasere, A., Omiecinski, E. and Navathe, S. An efficient algorithm for mining association rules in large databases. Proc 21st VLDB Conference, Zurich, 432–444. 1995.
Toivonen, H. Sampling large databases for association rules. Proc 22nd VLD-B Conference, Bombay, 1996.
Rymon, R. Search Through Systematic Set Enumeration. Proc. 3rd Int’l Conf. on Principles of Knowledge Represenation and Reasoning. 539–550.
Coenen, F. (1999). Partial Support. Dept of Computer Science, University of Liverpool. Working Paper 4. http://www.csc.liv.ac.uk/~graham_g/kdfm.html.
Coenen, F. (1999). Partial Support Using a “Partial Support Tree”. Dept of Computer Science, University of Liverpool. Working Paper 5. http://www.csc.liv.ac.uk/~graham_g/kdfm.html.
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© 2000 Springer-Verlag London Limited
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Goulbourne, G., Coenen, F., Leng, P. (2000). Algorithms for Computing Association Rules Using a Partial-Support Tree. In: Bramer, M., Macintosh, A., Coenen, F. (eds) Research and Development in Intelligent Systems XVI. Springer, London. https://doi.org/10.1007/978-1-4471-0745-3_9
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DOI: https://doi.org/10.1007/978-1-4471-0745-3_9
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