Abstract
Rough set theory has been widely used in knowledge acquisition. However, In conventional application of rough set to numeric data, data must be pre-classified. In this paper, a different approach is introduced to deal with numeric data. We develop a mining algorithm based on fuzzy sets and tolerance rough set model, which offers a way of relating data in their semantics.
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© 2005 Springer-Verlag Berlin Heidelberg
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Lee, HS., Shen, PD., Chyr, WL., Tseng, WK. (2005). Mining Quantitative Data Based on Tolerance Rough Set Model. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2005. Lecture Notes in Computer Science(), vol 3681. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11552413_52
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DOI: https://doi.org/10.1007/11552413_52
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28894-7
Online ISBN: 978-3-540-31983-2
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