Abstract
The main goal of data mining is to mine knowledge from large scale of datasets. And the key requirement of data mining is concise representation, which is consistent with the notion of minimal reduction in Rough Sets. This paper will regard RS as such a tool to implement the key requirement. First, we will introduce the basic problem in rough set theory — attribute reduction. Second, data enriching for UCI repository is analyzed with the measures called Evaporation Rate of Attribute, Object and Data respectively. Third, a rule+exception model is given to explain the set of learning data. Finally, some more complicated problems are discussed and the conclusions are given.
Research supported by 863 program and NSF of China.
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Wang, J. (1999). Rough Sets and Their Applications in Data Mining. In: Chen, G., Ying, M., Cai, KY. (eds) Fuzzy Logic and Soft Computing. The International Series on Asian Studies in Computer and Information Science, vol 6. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-5261-1_12
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DOI: https://doi.org/10.1007/978-1-4615-5261-1_12
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