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19. Catlett, J.: On changing continuous attributes into ordered discrete attributes. In European
Working Session on Learning (EWSL), Lecture Notes on Computer Science, vol. 482, pp.
164-178. Springer (1991)
20. Cerquides, J., Mantaras, R.L.D.: Proposal and empirical comparison of a parallelizable
distance-based discretization method. In: Proceedings of the Third International Conference
on Knowledge Discovery and Data Mining (KDD), pp. 139-142 (1997)
21. Chan, C., Batur, C., Srinivasan, A.: Determination of quantization intervals in rule based model
for dynamic systems. In: Proceedings of the Conference on Systems and Man and and Cyber-
netics, pp. 1719-1723 (1991)
22. Chao, S., Li, Y.: Multivariate interdependent discretization for continuous attribute. Proc. Third
Int. Conf. Inf. Technol. Appl. (ICITA) 2 , 167-172 (2005)
23. Chen, C.W., Li, Z.G., Qiao, S.Y.,Wen, S.P.: Study on discretization in rough set based on genetic
algorithm. In: Proceedings of the Second International Conference on Machine Learning and
Cybernetics (ICMLC), pp. 1430-1434 (2003)
24. Ching, J.Y., Wong, A.K.C., Chan, K.C.C.: Class-dependent discretization for inductive learning
from continuous and mixed-mode data. IEEE Trans. Pattern Anal. Mach. Intell. 17 , 641-651
(1995)
25. Chlebus, B., Nguyen, S.H.: On finding optimal discretizations for two attributes. Lect. Notes
Artif. Intell. 1424 , 537-544 (1998)
26. Chmielewski, M.R., Grzymala-Busse, J.W.: Global discretization of continuous attributes as
preprocessing for machine learning. Int. J. Approximate Reasoning 15 (4), 319-331 (1996)
27. Chou, P.A.: Optimal partitioning for classification and regression trees. IEEE Trans. Pattern
Anal. Mach. Intell. 13 , 340-354 (1991)
28. Cios, K.J., Kurgan, L.A., Dick, S.: Highly scalable and robust rule learner: performance eval-
uation and comparison. IEEE Trans. Syst. Man Cybern. Part B 36 , 32-53 (2006)
29. Cios, K.J., Pedrycz, W., Swiniarski, R.W., Kurgan, L.A.: DataMining: AKnowledge Discovery
Approach. Springer, New York (2007)
30. Clarke, E.J., Barton, B.A.: Entropy andMDL discretization of continuous variables for bayesian
belief networks. Int. J. Intell. Syst. 15 , 61-92 (2000)
31. Cohen, J.A.: Coefficient of agreement for nominal scales. Educ. Psychol. Measur. 20 , 37-46
(1960)
32. Cohen, W.W.: Fast Effective Rule Induction. In: Proceedings of the Twelfth International
Conference on Machine Learning (ICML), pp. 115-123 (1995)
33. Dai, J.H.: A genetic algorithm for discretization of decision systems. In: Proceedings of the
Third International Conference on Machine Learning and Cybernetics (ICMLC), pp. 1319-
1323 (2004)
34. Dai, J.H., Li, Y.X.: Study on discretization based on rough set theory. In: Proceedings of the
First International Conference onMachine Learning and Cybernetics (ICMLC), pp. 1371-1373
(2002)
35. Demšar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res.
7 , 1-30 (2006)
36. Dougherty, J., Kohavi, R., Sahami, M.: Supervised and unsupervised discretization of contin-
uous features. In: Proceedings of the Twelfth International Conference on Machine Learning
(ICML), pp. 194-202 (1995)
37. Elomaa, T., Kujala, J., Rousu, J.: Practical approximation of optimal multivariate discretization.
In: Proceedings of the 16th International Symposium onMethodologies for Intelligent Systems
(ISMIS), pp. 612-621 (2006)
38. Elomaa, T., Rousu, J.: General and efficient multisplitting of numerical attributes. Mach. Learn.
36 , 201-244 (1999)
39. Elomaa, T., Rousu, J.: Necessary and sufficient pre-processing in numerical range discretiza-
tion. Knowl. Inf. Syst. 5 , 162-182 (2003)
40. Elomaa, T., Rousu, J.: Efficient multisplitting revisited: Optima-preserving elimination of par-
tition candidates. Data Min. Knowl. Disc. 8 , 97-126 (2004)
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