A data clustering algorithm for stratified data partitioning in artificial neural network

Expert Systems with Applications

Volumn 39, Issue 8, 2012, Pages 7004-7014

  Sahoo, Ajit K ^a   Zuo, Ming J ^a   Tiwari, M K ^b  

^a UNIVERSITY OF ALBERTA   (Canada)

^b INDIAN INSTITUTE OF TECHNOLOGY   (India)

Author keywords

Artificial neural network; Custom design clustering algorithm; Data clustering; Data partitioning; Fuzzy clustering; Genetic algorithm; Self organizing map

Indexed keywords

ARTIFICIAL NEURAL NETWORK; CUSTOM DESIGN; DATA CLUSTERING; DATA PARTITIONING; SELF ORGANIZING;

FUZZY CLUSTERING; GENETIC ALGORITHMS; OPTIMIZATION; SELF ORGANIZING MAPS;

CLUSTERING ALGORITHMS;

EID: 84857653016     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2012.01.047     Document Type: Article

References (40)

1. J.C. Bezdec Pattern Recognition with Fuzzy Objective Function Algorithms 1981 Plenum Press New York
2. Blake, C. L. & Merz, C. J. (1998). UCI repository of machine learning databases. Irvine, CA: Dept. Inform. Comput. Sci.; Univ. California.
3. G.J. Bowden, H.R. Maier, and G.C. Dandy Optimal division of data for neural network models in water resources applications Water Resources Research 38 2 2002 2-1 2-11
4. L. Chen, T. Sugi, S. Shirakawa, J. Zou, and M. Nakamura Feature extraction for mental fatigue and relaxation states based on systematic evaluation considering individual difference IEEJ Transactions on Electronics, Information and Systems 129 2 2009 302 307 +16
5. W.G. Cochran Sampling Techniques 1977 Wiley New York
6. B.S. Duran, and P.L. Odell Cluster Analysis 1974 Springer Berlin
7. D. Fletcher, and E. Goss Forecasting with neural networks: An application using bankruptcy data Information & Management 24 3 1993 159 167
8. M.T. Hagan, H.B. Demuth, and M. Beale Neural network design 1996 PWS Publishing Company Boston, MA
9. L. He, G.H. Huang, G.M. Zeng, and H.W. Lu Wavelet-based multi resolution analysis for data cleaning and its application to water quality management systems Expert Systems with Applications 35 3 2008 1301 1310
10. D. Joo, D. Choi, and H. Park The effects of data preprocessing in the determination of coagulant dosing rate Water Research 34 2000 3295 3302
11. Y. Linde, A. Buzo, and R.M. Gray An algorithm for vector quantizer design IEEE Transactions on Communications 28 1 1980 84 95
12. L. Kaufman, and P.J. Rousseeuw Finding groups in data. An introduction to cluster analysis 1990 John Wiley & Sons
13. T. Kohonen Self organizing maps Springer series in information sciences Vol. 30 2001 Springer Berlin, Heidelberg, New York
14. J.B. MacQueen Some methods for classification and analysis of multivariate observations Proceedings of 5th Berkeley symposium on mathematical statistics and probability 1967 University of California Press 281 297
15. H.R. Maier, and G.C. Dandy Neural networks for the prediction and forecasting of water resources variables: A review of modeling issues and applications Environmental Model Software 15 2000 101 124
16. R.J. May, H.R. Maier, and G.C. Dandy Data splitting for artificial neural networks using SOM-based stratified sampling Neural Networks 23 2 2010 283 294
17. A.W. Minns, and M.J. Hall Artificial neural networks as rainfall-runoff models Hydrological Sciences Journal 41 3 1996 399 417
18. Nedeljkovic, V.; & Milosavljevic, M. (1992). On the influence of the training set data preprocessing on neural networks training. In Proceedings of 11th IAPR international conference pattern recognition (pp. 1041-1045).
19. Nguyen, H. H.; & Chan, C. W. (2004). A comparison of data preprocessing strategies for neural network modeling of oil production prediction. In Proceedings of third IEEE international conference on cognitive informatics.
20. M.H. Nguyen, and F. Torre Optimal feature selection for support vector machines Pattern Recognition 43 3 2010 584 591
21. O.E. Noord The influence of data preprocessing on the robustness and parsimony of multivariate calibration models Chemometrics and Intelligent Laboratory Systems 23 1994 65 70
22. S.S. Park, Y.G. Shin, and D.S. Jang A novel efficient technique for extracting valid feature information Expert Systems with Applications 37 3 2010 2654 2660
23. P.J. Rousseeuw Silhouettes: A graphical aid to the interpretation and validation of cluster analysis Computational and Applied Mathematics 20 1987 53 65
24. Sahoo, A. K.; Zhang, Y.; & Zuo, M. J. (2008). Estimating crack size and location in a steel plate using ultrasonic signals and CFBP neural networks, CCECE, Niagara Falls, Canada.
25. Sahoo, A. K. (2011). A data clustering algorithm for stratified data partitioning in artificial neural network. M.Sc. thesis, Department of Mechanical Engineering, University of Alberta, Spring.
26. B. Samanta, S. Bandopadhyay, R. Ganguli, and S. Dutta Sparse data division using data segmentation and Kohonen network for neural network and geostatistical ore grade modeling in nome offshore placer deposit Natural Resources Research 13 3 2004 189 200
27. B. Samanta, S. Bandopadhyay, and R. Ganguli Data segmentation and genetic algorithms for sparse data division in nome placer gold grade estimation using neural network and geostatistics Exploration and Mining Geology 11 1-4 2004 69 76
28. M.A. Shahin, H.R. Maier, and M.B. Jaksa Data division for developing neural networks applied to geotechnical engineering Journal of Computing in Civil Engineering 18 2 2004 105 114
29. J. Sjoberg Regularization as a substitute for preprocessing of data in neural network training Artificial Intelligence in Real-Time Control 1992 31 35
30. R. Stein Selecting data for neural networks AI Expert 8 2 1993 42 47
31. R. Stein Preprocessing data for neural networks AI Expert 8 3 1993 32 37
32. Tarassenko, L. (1998). A guide to neural computing applications. Neural Computing Applications Forum.
33. A.S. Tokar, and P.A. Johnson Rainfall-runoff modeling using artificial neural network Journal of Hydraulic Engineering 4 3 1999 232 239
34. F. Tong, and X. Liu Samples selection for artificial neural network training in preliminary structural design Tsinghua Science and Technology 10 2 2005 233 239
35. J.M. Twomey, and A.E. Smith Bias and variance of validation methods for function approximation neural networks under conditions of sparse data IEEE Transactions on Systems, Man and Cybernetics, Part C: Applications and Reviews 28 3 1998 417 430
36. Y. Xu, D. Zhang, and J.Y. Yang A feature extraction method for use with bimodal biometrics Pattern Recognition 43 3 2010 1106 1115
37. G.G. Yen, and K.C. Lin Wavelet packet feature extraction for vibration monitoring IEEE Transactions on Industrial Electronics 47 3 2000 650 667
38. K.A. Yoon, and D.H. Bae A pattern-based outlier detection method identifying abnormal attributes in software project data Information and Software Technology 52 2 2010 137 151
39. L. Yu, S.Y. Wang, and K.K. Lai Foreign-exchange-rate forecasting with artificial neural networks 2007 Springer New York
40. X. Zhang, and C. Sun Dynamic intelligent cleaning model of dirty electric load data Energy Conversion and Management 49 4 2008 564 569