Abstract
Theory of Rough Sets provides good foundations for the attribute reduction processes in data mining. For numeric attributes, it is enriched with appropriately designed discretization methods. However, not much has been done for symbolic attributes with large numbers of values. The paper presents a framework for the symbolic value partition problem, which is more general than the attribute reduction, and more complicated than the discretization problems. We demonstrate that such problem can be converted into a series of the attribute reduction phases. We propose an algorithm searching for a (sub)optimal attribute reduct coupled with attribute value domains partitions. Experimental results show that the algorithm can help in computing smaller rule sets with better coverage, comparing to the standard attribute reduction approaches.
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References
Pawlak, Z.: Rough sets. International Journal of Computer and Information Sciences 11, 341–356 (1982)
Nguyen, S.H.: Regularity Analysis And Its Application In Data Mining. PhD thesis, Warsaw University, Warsaw, Poland (1999)
Min, F., Liu, Q., Fang, C.: Rough Sets Approach to Symbolic Value Partition. International Journal on Approximate Reasoning (Submitted 2007)
Nguyen, H.S.: Discretization of Real Value Attributes, Boolean Reasoning Approach. PhD thesis, Warsaw University, Warsaw, Poland (1997)
Min, F., Liu, Q., Tan, H., Chen, L.: The M-relative reduct problem. In: Wang, G.-Y., Peters, J.F., Skowron, A., Yao, Y. (eds.) RSKT 2006. LNCS (LNAI), vol. 4062, pp. 170–175. Springer, Heidelberg (2006)
Pawlak, Z.: Some issues on rough sets. In: Peters, J.F., Skowron, A., Grzymała-Busse, J.W., Kostek, B., Świniarski, R.W., Szczuka, M.S. (eds.) Transactions on Rough Sets I. LNCS, vol. 3100, pp. 1–58. Springer, Heidelberg (2004)
Ganter, B., Wille, R. (eds.): Formal Concept Analysis: Mathematical Foundations. Springer, New York (1999)
Blake, C.L., Merz, C.J.: UCI repository of machine learning databases (1998), http://www.ics.uci.edu/~mlearn/mlrepository.html
Bazan, J., Szczuka, M.: The RSES homepage (1994–2005), http://logic.mimuw.edu.pl/~rses
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Min, F., Liu, Q., Fang, C., Zhang, J. (2007). Reduction Based Symbolic Value Partition. In: Szczuka, M.S., et al. Advances in Hybrid Information Technology. ICHIT 2006. Lecture Notes in Computer Science(), vol 4413. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77368-9_3
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DOI: https://doi.org/10.1007/978-3-540-77368-9_3
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