Abstract
In this paper problem of mining data with weights and finding association rules is presented. Some applications are discussed, especially focused on financial data. Solutions of the problem are analyzed. A few approaches are proposed and compared. Pruning based on measures of rules interestingness is described and some measures proposed in literature are shown. Influence of data weights on these measures is also discussed.
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References
Agrawal R., Imielinski T., Swami A.: Mining Associations Rules between Sets of Items in Large Databases; Proc. of the ACM SIGMOD Conf. on Management of Data. Washington DC 1993.
Agrawal R., Srikant R.: Fast Algorithms for Mining Association Rules in Large Databases Int’l Conf. on VLDs; Santiago, Chile, 1994.
Agrawal R., Srikant R.: Mining Association Rules. Proc. of the 21s` VLDB Conf. Zurich Switzerland 1995
Bayardo R.J. Jr.: Efficiently Mining Long Patterns from Databases; ACMSIGMOD Int’l Conf. on Management of Data 1998.
Bayardo R.J. Jr., Agrawal R.: Mining the Most Interesting Rules; ACM SIGKDD Int’l Conf. on Knowledge Discovery and Data Miningl999.
Bayardo R.J. Jr., Agrawal R., Gunopulos D.: Constraint-Based Mining in Large, Dense Databases; Int’l Conf. on Data Engineering, 1999.
Kryszkiewicz M.: Representative Association Rules; Proc. of PAKDD 1998 Melbourne, Australia
Kryszkiewicz M.: Representative Association Rules and Minimum Condition Maximum Consequence Association Rules; Proc. of PAKDD 1998 Nantes, France
Savasere A., Omiecinski E., Navathe S.: An Efficient Algorithm for Mining Association Rules in Large Databases; In Proc. of the 21st Int’l Conf. on Very Large Data-Bases Zurich 1995.
Toivonen H., Klemettinen M., Ronkainen P., Hätönen K., Mannila H.: Pruning and Grou** Discovered Association Rules; Minet Workshop on Statistics, Machine Learning and Discovery in Databases; Heraklion, Create, Greece 1995
Zaki M.J.: Generating Non-Redundant Association Rules; KDD 2000 Boston MA USA
Hilderman, R. J., Hamilton, H. J. (2001). Evaluation of interestingness measures for ranking discovered knowledge. Proceedings of the Fifth Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 247–259 ). Hong Kong, China: Springer-Verlag.
B. Iglesia “Induction of Interesting Rules from Large Datasets”, University of East Anglia, 1999.
S. Brin, R. Motwani, J. D. Ullman, S. Tsur „Dynamic Itemset Counting and Implication Rules for Market Basket Data“, 1997.
B. Gray, M.E. Orlowska “Clustering categorical attributes into interesting association rules”. Proceedings of the Second Pacific-Asia Conference on PAKDD’ 1998.
P. Smyth and R.M. Goodman “Rule induction using information theory”. “Knowledge Discovery in Databases”, 1991.
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Sołdacki, P., Protaziuk, G. (2002). Discovering Interesting Rules from Financial Data. In: Kłopotek, M.A., Wierzchoń, S.T., Michalewicz, M. (eds) Intelligent Information Systems 2002. Advances in Soft Computing, vol 17. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1777-5_11
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DOI: https://doi.org/10.1007/978-3-7908-1777-5_11
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-7908-1509-2
Online ISBN: 978-3-7908-1777-5
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