Abstract
A parallel rule-extracting algorithm based on the improved discernibility matrix [2] is proposed, by this way, a large amount of raw data can be divided into some small portions to be processed in parallel. The confidence factor is also introduced to the rule sets to obtain the uncertainty rules. The most important advantage of this algorithm is that it does not need to calculate the discernibility matrix corresponding to these overall data.
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© 2004 Springer-Verlag Berlin Heidelberg
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Yong, L., Congfu, X., Yunhe, P. (2004). A Parallel Approximate Rule Extracting Algorithm Based on the Improved Discernibility Matrix. In: Tsumoto, S., Słowiński, R., Komorowski, J., Grzymała-Busse, J.W. (eds) Rough Sets and Current Trends in Computing. RSCTC 2004. Lecture Notes in Computer Science(), vol 3066. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25929-9_60
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DOI: https://doi.org/10.1007/978-3-540-25929-9_60
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22117-3
Online ISBN: 978-3-540-25929-9
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