A new method for mining regression classes in large data sets

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 23, Issue 1, 2001, Pages 5-21

  Leung, Yee ^a,b   Ma, Jiang Hong ^a,b   Zhang, Wen Xiu ^a,b  

^a CHINESE UNIVERSITY OF HONG KONG   (Hong Kong)

^b XI'AN JIAOTONG UNIVERSITY   (China)

Author keywords

Data mining; Genetic algorithm; Maximum likelihood method; Mixture modeling; Rcmd method; Regression class; Robustness

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; DATABASE SYSTEMS; GENETIC ALGORITHMS; LEARNING SYSTEMS; MAXIMUM LIKELIHOOD ESTIMATION; OPTIMIZATION; PATTERN RECOGNITION; REGRESSION ANALYSIS;

MIXTURE MODELLING; REGRESSION CLASS MIXTURE DECOMPOSITION;

DATA MINING;

EID: 0035119388     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/34.899943     Document Type: Article

References (35)

1. Clustering and Classification, P. Arabie, L.J. Hubert, and G. De Soete, eds. Singapore: World Scientific, 1996.
2. Intelligent Data Analysis: An Introduction, M. Berthold and D.J. Hand, eds. New York: Springer, 1999.
3. S.B. Caudill and R.N. Acharya,Maximum-Likelihood Estimation of a Mixture of Normal Regressions: Starting Values and Singularities, Comm. Statistics-Simulation, vol. 27, no.3, pp. 667-674, 1998.
4. V. Cherkassky and F. Mulier, Learning from Data: Concepts, Theory, and Methods. New York: John Wiley & Sons, 1998.
5. Advances in Intelligent Data Analysis, D.J. Hand, J.N. Kok, and M. Berthold, eds. Berlin: Heidelberg, Springer-Verlag, 1999.
6. C.-N. Hsu and C.A. Knoblock,Estimating the Robustness of Discovered Knowledge, Proc. First Int'l Conf. Knowledge Discovery and Data Mining, pp. 156-161, Aug. 1995.
7. G. Danuser and M. Stricker,Parametric Model Fitting: From Inlier Characterization to Outlier Detection, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 20, no. 2, pp. 263-280, Feb. 1998.
8. R.N. Dave and R. Krishnapuram,Robust Clustering Methods: A Unified View, IEEE Trans. Fuzzy Systems, vol. 5, no. 2, pp. 270-293, May 1997.
9. U. Fayyad and P. Stolorz,Data Mining and KDD: Promise and Challenges, Future Generation Computer Systems, vol. 13, pp. 99-115, 1997.
10. J.H. Friedman,An Overview of Predictive Learning and Function Approximation, From Statistics to Neural Networks: Theory and Pattern Recognition Applications, V. Cherkassky, J.H. Friedman, and H. Wechsler, eds., pp. 1-61. Berlin Heidelberg: Springer-Verlag, 1994.
11. C. Glymour, D. Madigan, D. Pregibon, and P. Symth,Statistical Themes and Lessons for Data Mining, Data Mining and Knowledge Discovery, vol. 1, pp.11-28, 1997.
12. D.J. Hand,Data Mining: Statistics and More? The Am. Statistician, vol. 52, no. 2, pp. 112-118, 1998.
13. R.J. Hathaway and J.C. Bezdek,Switching Regression Models and Fuzzy Clustering, IEEE Trans. Fuzzy Systems, vol. 1, no. 3, pp. 195-204, Aug. 1993.
14. M. Holsheimer and M. Kersten,A Perspective on Databases and Data Mining, Proc. First Int'l Conf. Knowledge Discovery and Data Mining, pp. 150-155, 1995.
15. J.R.M. Hosking, E.P.D. Pednault, and M. Sudan,A Statistical Perspective on Data Mining, Future Generation Computer Systems, vol. 13, pp. 117-134, 1997.
16. T. Imielinski and H. Mannila,A Database Perspective on Knowledge Discovery, Comm. ACM, vol. 39, pp. 58-64, 1996.
17. Q. Ji and R.M. Haralick,Breakpoint Detection Using Covariance Propagation, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 20, no. 8, pp. 845-951, Aug. 1998.
18. G.H. John,Robust Decision Tree: Removing Outliers from Databases, Proc. First Int'l Conf. Knowledge Discovery and Data Mining, pp. 174-179, Aug. 1995.
19. M.G. Kendall, Kendall Advanced Theory of Statistics, fifth ed. London: Charles Griffin, 1987.
20. E. Kim, M. Park, S. Ji, and M. Park,A New Approach to Fuzzy Modeling, IEEE Trans. Fuzzy Systems, vol. 5, no. 3, pp. 328-337, 1997.
21. K.-N. Lau, C.-H. Yang, and G.V. Post,Stochastic Preference Modeling within a Switching Regression Framework, Computers Ops. Res., vol. 23, no. 12, pp. 1163-1169, 1996.
22. G.J. McLachlan, Discriminant Analysis and Statistical Pattern Recognition. New York: John Wiley & Sons, 1992.
23. G.J. McLachlan and K.E. Basford, Mixture Models: Inference and Applications to Clustering, New York and Basel: Marcel Dekker Inc., 1988.
24. R.J. O'hara Hines,An Application of Retrospective Sampling in the Analysis of a Very Large Clustered Data Set, J. Statistical Computer Simulation, vol. 59, pp. 63-81, 1997.
25. Genetic Algorithms and Evolution Strategies in Engineering and Computer Science, D. Quagliarella, J. P'eriaux, C. Poloni, and G. Winter, eds. England: John Wiley & Sons, 1998.
26. R.E. Quandt and J.B. Ramsey,Estimating Mixtures of Normal Distributions and Switching Regressions, J. Am. Statistical Assoc., vol. 73, no. 364, pp. 730-738, 1978.
27. R.A. Redner and H.F. Walker,Mixture Densities, Maximum-Likelihood and the EM Algorithm, SIAM Rev., vol. 26, no. 2, pp. 195-239, 1984.
28. J.P. Royston,An Extension of Shapiro and Wilk's W Test for Normality to Large Samples, Applied Statistics, vol. 31, no. 2, pp. 115-124, 1982.
29. C.V. Stewart,MINPRAN: A New Robust Estimator for Computer Vision, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 17, no. 10, pp. 925-938, 1995.
30. D.M. Titterington, A.F.M. Smith, and U.E. Makov, Statistical Analysis of Finite Mixture Distributions. New York: John Wiley & Sons, 1987.
31. V.N. Vapnik, The Nature of Statistical Learning Theory. New York: Springer-Verlag, 1995.
32. R.D. De Veaux,Mixtures of Linear Regressions, Computational Statistics and Data Analysis, vol. 8, pp. 227-245, 1989.
33. X. Zhuang, Y. Huang, K. Palaniappan, and Y. Zhao,Gaussian Mixture Density Modeling, Decomposition, and Applications, IEEE Trans. Image Processing, vol. 5, no. 9, pp. 1293-1302, 1996.
34. X. Zhuang, T. Wang, and P. Zhang,A Highly Robust Estimator through Partial-Likelihood Function Modeling and Its Application in Computer Vision, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 14, no. 1, pp. 19-35, Jan. 1992.
35. B. Zupan and M. Bohanec,A Database Decomposition Approach to Data Mining and Machine Discovery, Proc. First Int'l Conf. Knowledge Discovery and Data Mining, pp. 299-303, 1997.