A comparison of six approaches to discretization-a rough set perspective

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5009 LNAI, Issue , 2008, Pages 31-38

  Blajdo, Piotr ^a   Grzymala Busse, Jerzy W ^b,c   Hippe, Zdzislaw S ^a   Knap, Maksymilian ^a   Mroczek, Teresa ^a   Piatek, Lukasz ^a  

^a UNIVERSITY OF INFORMATION TECHNOLOGY AND MANAGEMENT   (Poland)

^b University of Kansas   (United States)

^c INSTITUTE OF COMPUTER SCIENCE   (Poland)

Author keywords

Cluster analysis; Discretization; F test; Merging intervals; Rough sets; Ten fold cross validation; Test on the difference between means

Indexed keywords

CLUSTER ANALYSIS; DATA ACQUISITION;

DISCRETIZATION METHODS; MERGING INTERVALS; TEN-FOLD CROSS VALIDATION;

ROUGH SET THEORY;

EID: 44649105833     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-79721-0_10     Document Type: Conference Paper

References (15)

1. Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and Regression Trees, Wadsworth & Brooks, Monterey, CA (1984)
2. Chao, L.L.: Introduction to Statistics. Brooks Cole Publishing Co., Monterey (1980)
3. Chan, C.C., Grzymala-Busse, J.W.: On the attribute redundancy and the learning programs ID3, PRISM, and LEM2. Department of Computer Science, University of Kansas, TR-91-14 (1991)
4. Chmielewski, M.R., Grzymala-Busse, J.W.: Global discretization of continuous attributes as preprocessing for machine learning. Int. Journal of Approximate Reasoning 15, 319-331 (1996)
5. Dougherty, J., Kohavi, R., Sahami, M.: Supervised and unsupervised discretization of continuous features. In: 12-th International Conference on Machine Learning, pp. 194-202. Morgan Kaufmann, San Francisco (1995)
6. Everitt, B.: Cluster Analysis, 2nd edn. Heinmann Educational Books, London (1980)
7. Fayyad, U.M., Irani, K.B.: On the handling of continuous-valued attributes in decision tree generation. Machine Learning 8, 87-102 (1992)
8. Grzymala-Busse, J.W.: A new version of the rule induction system LERS. Fundamenta Informaticae 31, 27-39 (1997)
9. Grzymala-Busse, J.W.: Discretization of numerical attributes. In: Klösgen, W., Zytkow, J. (eds.) Handbook of Data Mining and Knowledge Discovery, pp. 218-225. Oxford University Press, New York (2002)
10. Grzymala-Busse, J.W.: MLEM2: A new algorithm for rule induction from imperfect data. In: 9th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems ESIA Annecy, France, pp. 243-250 (2002)
11. Grzymala-Busse, J.W., Stefanowski, J.: Three discretization methods for rule induction. Int. Journal of Intelligent Systems. 16, 29-38 (2001)
12. Pawlak, Z.: Rough Sets. International Journal of Computer and Information Sciences 11, 341-356 (1982)
13. Peterson, N.: Discretization using divisive cluster analysis and selected postprocessing techniques, University of Kansas, Internal Report. Department of Computer Science (1993)
14. Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Mateo (1993)
15. Stefanowski, J.: Handling continuous attributes in discovery of strong decision rules. In: Polkowski, L., Skowron, A. (eds.) RSCTC 1998. LNCS (LNAI), vol. 1424, pp. 394-401. Springer, Heidelberg (1998)