Abstract
Most discretization algorithms are univariate and consider only one attribute at a time. Stephen D. Bay presented a multivariate discretization(MVD) method that considers the affects of all the attributes in the procedure of data mining. But as the author mentioned, any test of differences has a limited amount of power. We present OMVD by improving MVD on the power of testing differences with a genetic algorithm. OMVD is more powerful than MVD because the former does not suffer from setting the difference threshold and from seriously depending on the basic intervals. In addition, the former simultaneously searches partitions for multiple attributes. Our experiments with some synthetic and real datasets suggest that OMVD could obtain more interesting discretizations than could MVD.
This work is funded by China National Natural Science Foundation grants 60442002 and 60443003.
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References
Agarwal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proc. ACM SIGMOD International Conference on Management of Data, Washington, DC, pp. 207–216 (1993)
Quinlan, J.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Francisco (1993)
Bay, S.D.: Multivariate discretization for set mining. Knowledge and Information Systems 3, 491–512 (2001)
Bay, S.D., Pazzani, M.J.: Detecting group differences: Mining contrast sets. Data Mining and Knowledge Discovery 5, 213–246 (2001)
Kwedlo, W., Kretowski, M.: An evolutionary algorithm using multivariate discretization for decision rule induction. In: Principles of Data Mining and Knowledge Discovery, pp. 392–397 (1999)
Srikant, R., Agrawal, R.: Mining quantitative association rules in large relational tables. In: Jagadish, H.V., Mumick, I.S. (eds.) Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data, Montreal, Quebec, Canada, pp. 1–12 (1996)
Miller, R.J., Yang, Y.: Association rules over interval data. In: Proceedings ACM SIGMOD International Conference on Management of Data, pp. 452–461 (1997)
Monti, S., Cooper, G.F.: A latent variable model for multivariate discretization. In: The 7th Int. Workshop Artificial Intelligence and Statistics, Fort Lauderdale (1999)
Ludl, M.C., Widmer, G.: Relative unsupervised discretization for association rule mining. In: Proceedings of the 4th European Conference on Principles and Practice of Knowledge Discovery in Databases, Springer, Berlin (2000)
Mehta, S., Parthasarathy, S., Yang, H.: Toward unsupervised correlation preserving discretization. IEEE Transactions on Knowledge and Data Engineering 17, 1174–1185 (2005)
Eiben, A., Smith, J.: Introduction to Evolutionary Computing. Springer, Heidelberg (2003)
Ruggles, S., Sobek, M., Alexander, T., et. al.: Integrated public use microdata series: Version 2.0 minneapolis: Historical census projects (1997)
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He, Z., Tian, S., Huang, H. (2006). OMVD: An Optimization of MVD. In: Li, X., Zaïane, O.R., Li, Z. (eds) Advanced Data Mining and Applications. ADMA 2006. Lecture Notes in Computer Science(), vol 4093. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11811305_52
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DOI: https://doi.org/10.1007/11811305_52
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37025-3
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