Hostname: page-component-78c5997874-s2hrs Total loading time: 0 Render date: 2024-11-08T08:38:42.827Z Has data issue: false hasContentIssue false
  • English
  • Français

DMiner-I: A software tool of data mining and its applications

Published online by Cambridge University Press:  06 September 2002

Jie Yang ,
Chenzhou Ye  and
Nianyi Chen
Jie Yang
Affiliation:
Institute of Image Processing & Pattern Recognition, Shanghai Jiaotong University, Shanghai 200030 (China)
Chenzhou Ye
Affiliation:
Institute of Image Processing & Pattern Recognition, Shanghai Jiaotong University, Shanghai 200030 (China)
Nianyi Chen
Affiliation:
Institute of Image Processing & Pattern Recognition, Shanghai Jiaotong University, Shanghai 200030 (China)
Get access Rights & Permissions [Opens in a new window]

Summary

A software tool for data mining (DMiner-I) is introduced, which integrates pattern recognition (PCA, Fisher, clustering, HyperEnvelop, regression), artificial intelligence (knowledge representation, decision trees), statistical learning (rough set, support vector machine), and computational intelligence (neural network, genetic algorithm, fuzzy systems). It consists of nine function models: pattern recognition, decision trees, association rule, fuzzy rule, neural network, genetic algorithm, HyperEnvelop, support vector machine and visualization. The principle, algorithms and knowledge representation of some function models of data mining are described. Nonmonotony in data mining is dealt with by concept hierarchy and layered mining. The software tool of data mining is realized by Visual C++ under Windows 2000. The software tool of data mining has been satisfactorily applied in the prediction of regularities of the formation of ternary intermetallic compounds in alloy systems, and diagnosis of brain glioma.

Keywords

Data miningKnowledge representationDecision treesBrain glioma diagnosis
Type
Research Article
Information
Robotica , Volume 20 , Issue 5 , September 2002 , pp. 499 - 508
Copyright
Copyright © Cambridge University Press 2002

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1. Anand, S. S., “Designing a Kernel for data mining”, IEEE Expert 12(2), 65–74 (1997).CrossRefGoogle Scholar
2. Quinlan, J. R., “Decision Trees and Decision making”, IEEE Transaction On Systems, Man, and Cybernetics 20(2), 339–346 (1990).Google Scholar
3. Chidanand, A. and Weiss, S., “Data mining with decision trees and decision rules”, Future Generation Computer Systems 13, 197–210 (1997).Google Scholar
4. Pawlak, Z., Rough Set, Theoretical Aspects of Reasoning About Data (Dordrecht, The Netherlands: Kluwer, 1991).Google Scholar
5. Agrawal, R., Mannila, H., Srikant, R., Toivonen, H. and Verkamo, A. I., “Fast Discovery of Association Rules”, Advances in Knowledge Discovery and Data Mining (U. M. Fayyad, ed.) (Morgan Kaufmann, San Mateo, CA). pp. 307–328.Google Scholar
6. Yang, J., Guo, Y. and Huang, X., “A Software Development System for Fuzzy Control”, Robotica 18(4), 375–380 (2000).CrossRefGoogle Scholar
7. Vapnik, V., The Nature of Statistical Learning Theory (the second edition) (Springer-Verlag, New York, 1998).Google Scholar
8. Murthy, S. K., Kasif, S. and Salzberg, S., “System for Induction of Oblique Decision Trees”, Journal of Artificial Intelligence Research 2, 1–32 (1994).Google Scholar
9. Tax, D. and Duin, R., “Support vector domain description”, Pattern Recognition Letters 20, 1191–1199 (1999).CrossRefGoogle Scholar
10. Yang, J., Ye, C. and Zhang, X., “An Expert system shell for fault diagnosis”, Robotica 19(5), 669–674 (2001).CrossRefGoogle Scholar