A star spectrum outlier mining system based on simulated annealing

International Journal of Innovative Computing, Information and Control

Volumn 4, Issue 9, 2008, Pages 2263-2271

  Cai, Jianghui ^a   Zhang, Jifu ^a   Zhao, Xujun ^a  

^a TAIYUAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

Author keywords

Clustering; Outliers; Simulated annealing; Star spectrum data; Visual simulation

Indexed keywords

EID: 63549134381     PISSN: 13494198     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (19)

1. E. M. Knorr and R. T. Ng, Algorithms for mining distance-based outliers in large datasets, Proc. of the 24th Int'l Conf. on Very Large Databases, New York, pp. 392-403, 1998.
2. C. Aggarwal and P. Yu, An effective and efficient algorithm for high-dimensional outlier detection, The VLDB Journal, vol. 14, no. 2, pp. 211-221, 2005.
3. Y. Z. Zhang, X. C. Yun, B. X. Fang and T. Zhang, A mining method for computer vulnerability correlation, International Journal of Innovative Computing, Information and Control, vol. 1, no. 1, pp. 43-51, 2005.
4. V. Barnett and T. Lewis, Outliers in Statistical Data, 2nd edition, NewYork, John Wiley and Sons, 1994.
5. H. Qi and J. Wang, A model for mining outliers from complex data sets, Proc. of the 19th Annual ACM Symposium on Applied Computing, Nicosia, pp. 595-599, 2004.
6. T. Johnson, I. Kwok and R. T. Ng, Fast computation of 2-dimensional depth contours, Proc. of the Fourth International Conference on Knowledge Discovery and Data Mining, New York, pp. 224-228, 1998.
7. A. K. Jain, M. N. Murty and P. J. Flynn, Data clustering: A review, ACM Comp. Surveys, vol. 31, no. 3, pp. 264-323, 1999.
8. E. Knorr, R. Ng, V. Tucakov, Distance-based outliers: Algorithms and applications, The VLDB Journal, vol. 8, no. 3, pp. 237-253, 2000.
9. C. Aggarwal, P. Yu, An effective and efficient algorithm for high-dimensional outlier detection, The VLDB Journal, vol. 14, no. 2, pp. 211-221, 2005.
10. J. Christopher and C. Burges, A tutorial on support vector machines for pattern recognition, Data Mining and Knowledge Discovery, vol. 2, no. 2, pp. 121-167, 1998.
11. C. Chuang, S. Su, J. T. Jeng and C. Hsiao, Robust support vector regression networks for function approximation with outliers, IEEE Tran. Neural Networks, vol. 13, no. 6, pp. 1322-1330, 2002.
12. D. P. Mukherjee, P. Pal and J. Das, Sodar image segmentation by fuzzy C-means, Signal Process, vol. 54, no. 3, pp. 295-301, 1996.
13. M. N. Ahmed, S. M. Yamany, N. Mohamed, A. A. Farag and T. Moriarty, A modified fuzzy Cmeans algorithm for bias field estimation and segmentation of mri data, IEEE Transactions on Medical Imaging, vol. 21, no. 3, pp. 193-200, 2002.
14. K. Umayahara, S. Miyamoto and Y. Nakamori, Formulations of fuzzy clustering for categorical data, International Journal of Innovative Computing, Information and Control, vol. 1, no. 1, pp. 83-94, 2005.
15. N. Metropolis, A. Rosenbluth, M. Rosenbluth, A. Teller and E. Teller, Equation of state calculations by fast computing machines, J. Chem. Phys, vol. 21, no. 6, pp. 1087-1092, 1953.
16. X. Y. Wang and J. M. Garibaldi, Simulated annealing fuzzy clustering in cancer diagnosis, European Journal of Informatica, vol. 29, no. 1, pp. 61-70, 2005.
17. X. Y. Wang, J. M. Garibaldi and T. Ozen, Application of the fuzzy C-means clustering method on the analysis of non pre-processed ftir data for cancer diagnosis, Proc. of the 8th Australian and New Zealand Conference on Intelligent Information Systems, Sydney, Australia, pp. 233-238, 2003.
18. M. Sato-Ilic, General class of weighted fuzzy cluster loading models, International Journal of Inno-vative Computing, Information and Control, vol. 4, no. 5, pp. 1023-1032, 2008.
19. X. Song, W. Chen and B. Jiang, Sample reducing method in support vector machine based on K-closest sub-clusters, International Journal of Innovative Computing, Information and Control, vol. 4, no. 7, pp. 1751-1760, 2008.