Mining Incomplete Data with Many Lost and Attribute-Concept Values
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Publisher
Springer International Publishing
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http://link.springer.com/content/pdf/10.1007/978-3-319-25754-9_9
Reference18 articles.
1. Clark, P.G., Grzymala-Busse, J.W.: Experiments on probabilistic approximations. In: Proceedings of the 2011 IEEE International Conference on Granular Computing, pp. 144–149 (2011)
2. Clark, P.G., Grzymala-Busse, J.W.: Rule induction using probabilistic approximations and data with missing attribute values. In: Proceedings of the 15-th IASTED International Conference on Artificial Intelligence and Soft Computing ASC 2012, pp. 235–242 (2012)
3. Lecture Notes in Computer Science;PG Clark,2013
4. Clark, P.G., Grzymala-Busse, J.W.: Mining incomplete data with lost values and attribute-concept values. In: Proceedings of the 2014 IEEE International Conference on Granular Computing, pp. 49–54 (2014)
5. Grzymala-Busse, J.W.: LERS–a system for learning from examples based on rough sets. In: Slowinski, R. (ed.) Intelligent Decision Support. Handbook of Applications and Advances of the Rough Set Theory, pp. 3–18. Kluwer Academic Publishers, Dordrecht (1992)
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1. Complexity of Rule Sets Induced from Data Sets with Many Lost and Attribute-Concept Values;Artificial Intelligence and Soft Computing;2016
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