Abstract
This paper presents some recent results of the research on the scalability of rough set based classification methods. The proposed solution is based on the close relationship between reduct calculation problem in rough set theory and association rule generation problem. This is a continuation of our previous results (see, e.g. [10] [11]). In this paper, the set of decision rules satisfying the test object is generated directly from the training data set. To make it scalable, we adopted the idea of the FP-growth algorithm for frequent item-sets [7], [6]. The experimental results on some benchmark data sets are showing the ability of the proposed solution to process a growing data sets.
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Kwiatkowski, P., Nguyen, S.H., Nguyen, H.S. (2010). On Scalability of Rough Set Methods. In: Hüllermeier, E., Kruse, R., Hoffmann, F. (eds) Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Methods. IPMU 2010. Communications in Computer and Information Science, vol 80. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14055-6_30
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DOI: https://doi.org/10.1007/978-3-642-14055-6_30
Publisher Name: Springer, Berlin, Heidelberg
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