A Comparison of Concept and Global Probabilistic Approximations Based on Mining Incomplete Data

Author:

Clark Patrick G.,Gao Cheng,Grzymala-Busse Jerzy W.,Mroczek Teresa,Niemiec Rafal

Publisher

Springer International Publishing

Reference21 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.: Experiments on rule induction from incomplete data using three probabilistic approximations. In: Proceedings of the 2012 IEEE International Conference on Granular Computing, pp. 90–95 (2012)

3. Clark, P.G., Grzymala-Busse, J.W.: An experimental comparison of three interpretations of missing attribute values using probabilistic approximations. In: Proceedings of the 14-th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, pp. 77–86 (2013)

4. Clark, P.G., Grzymala-Busse, J.W., Rzasa, W.: Mining incomplete data with singleton, subset and concept approximations. Inf. Sci. 280, 368–384 (2014)

5. Grzymala-Busse, J.W.: MLEM2: a new algorithm for rule induction from imperfect data. In: Proceedings of the 9th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, pp. 243–250 (2002)

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