Abstract
Data classification is an important research topic in the field of data mining and knowledge discovery. There have been many data classification methods studied, including decision-tree method, statistical methods, neural networks, rough sets, etc. In this paper, we present a new mathematical representation of qualitative concepts—Cloud Models. With the new models, mapping between quantities and qualities becomes much easier and interchangeable. Based on the cloud models, a novel qualitative strategy for data classification in large relational databases is proposed. Then, the algorithms for classification are developed, such as cloud generation, complexity reduction, identifying interacting attributes, etc. Finally, we perform experiments on a challenging medical diagnosis domain, acute abdominal pain. The results show the advantages of the model in the process of knowledge discovery.
Research was supported in part by Chinese National Advanced Technology Development Projects (No.863-306-ZT06-07-2).
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References
J.R. Quinlan, “Induction of decision trees,” Machine Learning, Vol. 1, 1986, pp81–106.
J.R. Quinlan, “C4.5: Programs for Machine Learning,” Morgan Kaufmann, 1993.
Shan, N., Ziarko, W., Hamilton, H.J., and Cercone, “Discovery Classification Knowledge in Databases Using Rough Sets”, In Proceedings of KDD-96: Second International Conference on Knowledge Discovery & Data Mining, Menlo Park, CA: AAAI Press, 1996.
M. Mehta, R. Agrawal, and J. Rissanen, “A fast scaleable classifier for data mining,” In Proc. of the Fifth Int’l Conference on Extending Database Technology, 1996.
A. Agrawal, S. ghosh, T. Imielinkski, B. Iyer, and A. Swami, “An Interval Classifier for Database Mining Applications,” Proceedings of the 18th International Conference on Very Large Data Bases, August 1992, p.560–573.
Li Deyi Shi Xuemei, Meng Haijun. “Membership clouds and clouds generators”, The Research and Development of Computers, 1995, 42(8):32–41.
D. Li, X. Shi, P. Ward and M.M. Gupta, “Soft Inference Mechanism Based on Cloud Models,” in Proceedings of the First International Workshop on Logic Programming and Soft Computing, Edited by Francesca Arcelli Fontana, Ferrante Formato and Trevor P. Martin, Bonn, Germany, Sept 6, 1996, p.38–62.
Deyi Li, Jiawei Han, Xuemei Shi, and Man Chung Chan, “Knowledge representation and discovery based on linguistic atoms”, Knowledge-Based Systems, Elsevier Science B.V. 10 (1998):431–440.
J. Han, Y. Cai, and N. Cercone, “Data-driven discovery of quantitative rules in relational databases”, IEEE Trans. Knowledge and Data Engineering, Vol. 3, 1993, p.29–40.
Y. Cai, N. Cercone, and J. Han, “Attribute-Oriented Induction in Relational Databases,” In Knowledge Discovery in Database, 1991, p.213–228.
Ziarko, W. And Shan, N. “On Discovery of Attribute Interactions and Domain Classifications”, In Lin. T.Y eds., Special Issue in Journal of Intelligent Automation and Soft Computing, 1995.
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© 1999 Springer-Verlag Berlin Heidelberg
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Fan, J., Li, D. (1999). Mining Classification Knowledge Based on Cloud Models. In: Zhong, N., Zhou, L. (eds) Methodologies for Knowledge Discovery and Data Mining. PAKDD 1999. Lecture Notes in Computer Science(), vol 1574. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48912-6_43
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DOI: https://doi.org/10.1007/3-540-48912-6_43
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