REFERENCES

data mining: opportunities and challenges
Chapter VI - Data Mining Based on Rough Sets
Data Mining: Opportunities and Challenges
by John Wang (ed) 
Idea Group Publishing 2003
Brought to you by Team-Fly

Beynon, M. (2000). An investigation of beta-reduct selection within variable precision rough sets model. 2nd International Conference on Rough Sets and Current Trends in Computing, Banff, 2000, LNAI 2005, pp. 82 90. Berlin: Springer-Verlag.

Booker, L. B., Goldberg, D. E., & Holland, J. F. (1990). Classifier systems and genetic algorithms, In J.G. Carbonell (ed.), Machine Learning. Paradigms and Methods, pp. 235 282. Cambridge, MA: MIT Press.

Budihardjo, A., Grzymala-Busse J., & Woolery, L. K. (1991). Program LERS_LB 2.5 as a tool for knowledge acquisition in nursing. 4th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, Koloa, Kauai, Hawaii, pp. 735 740, June 2 5, 1991, The University of Tennessee Space Institute Press.

Chan, C. C. & Grzymala-Busse, J. W. (1994). On the two local inductive algorithms: PRISM and LEM2. Foundations of Computing and Decision Sciences, 19, 185 203.

Freeman, R. L., Grzymala-Busse, J. W., Riffel, L. A., & Schroeder, S. R. (2001). Analysis of self-injurious behavior by the LERS data mining system. In the Proceedings of the Japanese Society for Artificial Intelligence International Workshop on Rough Set Theory and Granular Computing, RSTGC-2001, May 20 22, pp. 195 200, Matsue, Shimane, Japan. Bulletin Internet, Rough Set Society, 5(1/2).

Goodwin, L. K. & Grzymala-Busse, J. W. (2001). Preterm birth prediction/System LERS. In W. Kl sgen and J. Zytkow (eds.), Handbook of Data Mining and Knowledge Discovery. Oxford, UK: Oxford University Press.

Greco, S., Matarazzo, B., Slowinski, R., & Stefanowski, J. (2000). Variable consistency model of dominance-based rough sets approach. 2nd International Conference on Rough Sets, Banff, 2000, LNAI 2005, pp. 138 148. Berlin: Springer-Verlag.

Grzymala-Busse, D. M. & Grzymala-Busse, J. W. (1994). Evaluation of machine learning approach to knowledge acquisition. 14th International Avignon Conference, Paris, May 30-June 3, pp. 183 192, EC2 Press.

Grzymala-Busse, J. P., Grzymala-Busse, J. W., & Hippe, Z. S. (2001). Melanoma prediction using data mining system LERS. Proceedings of the 25th Anniversary Annual International Computer Software and Applications Conference COMPSAC 2001, Chicago, IL, October 8 12, pp. 615 620, IEEE Press.

Grzymala-Busse, J. W. (1987). Rough-set and Dempster-Shafer approaches to knowledge acquisition under uncertainty A comparison. Manuscript.

Grzymala-Busse, J. W. (1988). Knowledge acquisition under uncertainty a rough set approach. Journal of Intelligent & Robotic Systems 1, 1, 3 16.

Grzymala-Busse, J. W. (1991). Managing Uncertainty in Expert Systems. Boston, MA: Kluwer Academic Publishers.

Grzymala-Busse, J. W. (1992). LERS A system for learning from examples based on rough sets. In R. Slowinski (ed.), Intelligent decision support. Handbook of applications and advances of the rough set theory. pp. 3 18. Dordrecht, Boston, London: Kluwer Academic Publishers.

Grzymala-Busse, J. W. (1993). ESEP: An expert system for environmental protection. RSKD 93, International Workshop on Rough Sets and Knowledge Discovery, Banff, Alberta, Canada, October 12 15, pp. 499 508.

Grzymala-Busse, J. W. (1994). Managing uncertainty in machine learning from examples. Third Intelligent Information Systems Workshop, Wigry, Poland, pp. 70 84, IPI PAN Press.

Grzymala-Busse, J. W. (1995). Rough Sets. Advances in Imaging and Electron Physics 94, 151 195.

Grzymala-Busse, J. W. (1997). A new version of the rule induction system LERS. Fundamenta Informaticae 31, 27 39.

Grzymala-Busse, J. W. (1998a). Applications of the rule induction system LERS. In L. Polkowski and A. Skowron (eds.), Rough Sets in Knowledge Discovery 1, Methodology and Applications, pp. 366 375. Heidelberg-New York: Physica-Verlag.

Grzymala-Busse, J. W. (1998b). LERS A knowledge discovery system. In L. Polkowski and A. Skowron (eds.), Rough Sets in Knowledge Discovery 2, Applications, Case Studies and Software Systems. pp. 562 565. Heidelberg-New York: Physica-Verlag.

Grzymala-Busse, J. W. & Gunn, J. D. (1995). Global temperature analysis based on the rule induction system LERS. 4th Workshop on Intelligent Information Systems WIS'95, Augustow, Poland,. pp. 148 158, IPI PAN Press.

Grzymala-Busse, J. W. & Old, L. J. (1997). A machine learning experiment to determine part of speech from word-endings. 10th International Symposium on Methodologies for Intelligent Systems ISMIS'97, Charlotte, NC, October 15 18 , In Z. W. Ras and A. Skowron (eds.), Foundations of Intelligent Systems Lecture Notes in AI 1325, pp. 497 506. Berlin: Springer-Verlag.

Grzymala-Busse, J. W. & Wang, C. P. B. (1986). Classification and rule induction based on rough sets. 5th IEEE International Conference on Fuzzy Systems FUZZIEEE'96, New Orleans, Louisiana, September 8 11, pp. 744 747, IEEE Press.

Grzymala-Busse, J. W. & Werbrouck, P. (1998). On the best search method in the LEM1 and LEM2 algorithms. In E. Orlowska (ed.), Incomplete Information: Rough Set Analysis, pp. 75 91. Heidelberg-New York: Physica-Verlag.

Grzymala-Busse, J. W. & Woolery, L. K. (1994). Improving prediction of preterm birth using a new classification scheme and rule induction. 18th Annual Symposium on Computer Applications in Medical Care, SCAMC, Washington, DC, pp. 730 734, Hanley & Belfus, Inc. Publishers.

Grzymala-Busse, J. W. & Zou X. (1998). Classification strategies using certain and possible rules. 1st International Conference on Rough Sets and Current Trends in Computing,. Warsaw, Poland, June 22 26. Lecture Notes in Artificial Intelligence, No. 1424, 37 44 Berlin: Springer-Verlag.

Grzymala-Busse, J. W., Grzymala-Busse, W. J., & Goodwin, L. K. (1999). A closest fit approach to missing attribute values in preterm birth data. 7th International Workshop on Rough Sets, Fuzzy Sets, Data Mining and Granular-Soft Computing, RSFDGrC'99, Ube, Yamaguchi, Japan. Lecture Notes in Artificial Intelligence, No. 1711, 405 413, Berlin: Springer-Verlag.

Gunn, J. D. & Grzymala-Busse, J. W. (1994). Global temperature stability by rule induction: An interdisciplinary bridge. Human Ecology 22, 59 81.

Holland, J. H., Holyoak K. J., & Nisbett, R. E. (1986). Induction. Processes of inference, learning, and discovery. Cambridge, MA: MIT Press.

Katzberg, J. & Ziarko, W. (1996). Variable precision extension of rough sets. Fundamenta Informaticae, Special Issue on Rough Sets, 27, 155 168.

Kostek, B. (1998). Computer-based recognition of musical phrases using the rough set approach. Journal of Information Science, 104, 15 30.

Kryszkiewicz, M. (1994). Knowledge reduction algorithms in information systems. Ph.D. thesis, Faculty of Electronics, Warsaw University of Technology, Poland.

Loupe, P. S., Freeman, R. L., Grzymala-Busse, J. W., & Schroeder, S. R. (2001). Using rule induction for prediction of self-injuring behavior in animal models of development disabilities. 14th IEEE Symposium on Computer-Based Medical Systems, CBMS 2001, July 26 27, pp. 171 176, Bethesda, MD, IEEE Press.

Maheswari, U., Siromoney, A., Mehata, K., & Inoue, K. (2001). The variable precision rough set inductive logic programming model and strings. Computational Intelligence, 17, 460 471.

Michalski, R. S. (1983). A theory and methodology of inductive learning. In R.S. Michalski, J.G. Carbonell, & T.M. Mitchell (eds.): Machine Learning. An Artificial Intelligence Approach, pp. 83 134. San Francisco, CA: Morgan Kauffman.

Michalski, R.S., Carbonell J.G., & Mitchell, T.M. (1983). Machine learning. An artificial intelligence approach. San Francisco: CA: Morgan Kauffman.

Michalski, R. S., Mozetic, I., Hong, J. & Lavrac, N. (1986). The AQ15 inductive learning system: An overview and experiments. Report UIUCDCD-R-86-1260, Department of Computer Science, University of Illinois.

Moradi, H., Grzymala-Busse, J. W., & Roberts, J. A. (1995). Entropy of English text: Experiments with humans and a machine learning system based on rough sets. 2nd Annual Joint Conference on Information Sciences, JCIS'95, Wrightsville Beach, North Carolina, pp. 87 88, Paul R. Wang Press.

Mrozek, A. (1986). Use of rough sets and decision tables for implementing rule-based control of industrial processes. Bulletin of the Polish Academy of Sciences, 34, 332 356.

Nguyen, H. S. (1998). Discretization problems for rough set methods. 1st International Conference on Rough Sets and Current Trends in Computing, Warsaw, Poland. Lecture Notes in AI 1424, pp. 545 552. Berlin: Springer-Verlag.

Pawlak, Z. (1982). Rough Sets. International Journal of Computer and Information Sciences, 11, 341 356.

Pawlak, Z. (1984). Rough Sets. International Journal of Man-Machine Studies 20, 469.

Pawlak, Z. (1991). Rough Sets. Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Dordrecht, Boston, London.

Pawlak, Z. (1996). Rough sets, rough relations and rough functions. Fundamenta Informaticae, 27, 103 108.

Pawlak, Z., Grzymala-Busse, J. W., Slowinski, R., & Ziarko, W. (1995). Rough sets. Communications of the ACM 38, 89 95.

Peters, J. Skowron, A. & Suraj, Z. (1999). An application of rough set methods in control design. Workshop on Concurrency, Warsaw, pp. 214 235.

Plonka, L. & Mrozek, A. (1995). Rule-based stabilization of the inverted pendulum. Computational Intelligence, 11, 348 356.

Polkowski, L. & Skowron, A. (1998). Rough sets in knowledge discovery, 2, Applications, case studies and software systems, Appendix 2: Software Systems. pp. 551 601. Heidelberg-New York: Physica Verlag.

Shafer, G. (1976). A mathematical theory of evidence. Princeton, NJ: Princeton University Press.

Stefanowski, J. (1998). On rough set based approaches to induction of decision rules. In L. Polkowski & A. Skowron (eds.), Rough sets in data mining and knowledge discovery, pp. 500 529. Heidelberg-New York: Physica-Verlag.

Woolery, L., Grzymala-Busse, J., Summers, S., & Budihardjo, A. (1991). The use of machine learning program LERS_LB 2.5 in knowledge acquisition for expert system development in nursing. Computers in Nursing 9, 227 234.

Zhao, Z. (1993). Rough set approach to speech recognition. M.Sc. thesis, Computer Science Department University of Regina, Canada.

Ziarko, W. (1993). Variable precision rough sets model. Journal of Computer and Systems Sciences, 46, 39 59.

Ziarko, W. (1998a). Approximation region-based decision tables. International Conference on Rough Sets and Current Trends in Computing, Warsaw, Lecture Notes in AI 1424, pp. 178 185. Berlin: Springer-Verlag.

Ziarko, W. (1998b). KDD-R: Rough sets-based data mining system. In L. Polkowski & A. Skowron (eds.), Rough sets in knowledge discovery, Part II. Studies in Fuzziness and Soft Computing, pp. 598 601. Berlin: Springer-Verlag.

Ziarko, W. (1999). Decision making with probabilistic decision tables. 7th International. Workshop on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, RSFDGrC'99, Yamaguchi, Japan, Lecture Notes in AI 1711, pp. 463 471. Berlin, Springer-Verlag.

Ziarko, W. & Shan, N. (1996). A method for computing all maximally general rules in attribute-value systems. Computational Intelligence: an International Journal, 12, 223 234.

Brought to you by Team-Fly


Data Mining(c) Opportunities and Challenges
Data Mining: Opportunities and Challenges
ISBN: 1591400511
EAN: 2147483647
Year: 2003
Pages: 194
Authors: John Wang

flylib.com © 2008-2017.
If you may any questions please contact us: flylib@qtcs.net