Improved Bidirectional CABOSFV Based on Multi-Adjustment Clustering and Simulated Annealing

1. Ning, D., H. Li, H. Wang. Analysis and Prediction of Logistics Enterprise Competitiveness by Using a Real GA-Based Support Vector Machine. – Journal of System and Management Sciences, Vol. 3, 2013, No 2, pp. 29-34.Search in Google Scholar

2. Ju, C., F. Guo. Distributed Data Mining Model Based on Support Vector Machines. – Systems Engineering-Theory & Practice, Vol. 30, 2010, No 10, pp. 1855-1863.Search in Google Scholar

3. Wu, S., X. Gao. CABOSFV Algorithm for High Dimensional Sparse Data Clustering. – Journal of University Science & Technology Beijing, Vol. 11, 2004, No 3, pp. 283-288.Search in Google Scholar

4. Wu, S., W. Zhang, H. Huang, Y. Ye. FD-CABOSFV High Dimensional Data Clustering for Interval-Scaled Variables. – China Journal of Information Systems, Vol. 9, 2011, pp. 77-87.Search in Google Scholar

5. Wang, D., D. Zhu. Research of Mining Word Category Knowledge Based on CABOSFV. – Computer Science, Vol. 40, 2013, No 9, pp. 211-215.Search in Google Scholar

6. Pan, J. DS CABOSFV Clustering Algorithm for High Dimensional Data Stream. – In: 4th International Conference on Awareness Science and Technology (ICAST’12), 2012, pp. 16-19.Search in Google Scholar

7. Wu, S., Y. Ye, X. Yu. Clustering for High Dimensional Data Based on Extended Set Dissimilarity. – Application Research of Computers, Vol. 28, 2011, No 9, pp. 3253-3255.Search in Google Scholar

8. Wei, G., L. Zou, J. Pan. Improved Text Classification Algorithm for Spam Filtering Based on CABOSFV. – Future Computer & Information Technology, Vol. 86, 2013, pp. 1131-1139.10.2495/ICFCIT131301Search in Google Scholar

9. Zhang, Q. Research and Implementation of Clustering Analysis Algorithms Based on I-MINER. – In: 2013 International Conference on Computer Sciences and Applications, ICCSA’13, 2013, pp. 254-257.Search in Google Scholar

10. Song, Y., Q. Xiao. The Method of How to Determine Threshold Value of Set-Square-Difference in CABOSFV Algorithm. – Ship Science and Technology, Vol. 28, 2006, No 1, pp. 99-102.Search in Google Scholar

11. Zhu, Q., G. Tu, X. Gao, S. Wu, H. Chen. Enhanced CABOSFV Clustering Algorithm Based on Adaptive Threshold. – In: International Conference on Computer Science and Automation Engineering, ICCSAE’11, 2011, pp. 620-622.Search in Google Scholar

12. Gao, X., M. Yang, L. Li. Bidirectional CABOSFV for High Dimensional Sparse Data Clustering. – In: 2016 International Conference on Logistics, Informatics and Service Sciences, LISS’2016, 2016 (in Publishing).10.1109/LISS.2016.7854473Search in Google Scholar

13. Zhu, Q., X. Gao, S. Wu, M. Chen, H. Chen. High Dimensional Sparse Data Clustering Based on Sorting Idea. – Computer Engineering, Vol. 36, 2010, No 22, pp. 13-14.Search in Google Scholar

14. Wu, S., X. Feng, Q. Wu. Parallel Clustering Algorithm Based on Sparse Index Sort of High Dimensional Data. – Systems Engineering-Theory & Practice, Vol. S2, 2011, pp. 13-18.Search in Google Scholar

15. Wu, S., J. Wang, Y. Tan. Improved CABOSFV Clustering Considering Data Sort. – Computer Engineering & Applications, Vol. 47, 2011, No 34, pp. 127-129.Search in Google Scholar

16. Wu, S., Q. Wang, M. Jiang, Q. Wei. Clustering Algorithm of Categorical Data in Consideration of Sorting by Weight. – Journal of University of Science & Technology Beijing, Vol. 35, 2013, No 8, pp. 1093-1098.Search in Google Scholar

17. Kirkpatrick, S., C. D. Gelatt, M. P. Vecchi. Optimization by Simulated Annealing. – Science, Vol. 220, 1983, No 4598, pp. 671-680.10.1126/science.220.4598.67117813860Search in Google Scholar

18. Robini, M. C., P. J. Reissman. From Simulated Annealing to Stochastic Continuation: A New Trend in Combinatorial Optimization. – Glob. Optim., Vol. 56, 2013, No 1, pp. 185-215.10.1007/s10898-012-9860-0Search in Google Scholar

19. Guodong, Yu et al. Research on the Time Optimization Model Algorithm of Customer Collaborative Product Innovation. – Journal of Industrial Engineering & Management, Vol. 10, 2014, No 1, pp. 4666-4672.10.3926/jiem.838Search in Google Scholar

20. Delgoshaei, A., M. K. M. Ariffin, B. T. H. T. Baharudin. Pre-Emptive Resource-Constrained Multimode Project Scheduling Using Genetic Algorithm: A Dynamic Forward Approach. – Journal of Industrial Engineering & Management, Vol. 9, 2016, No 3, pp. 732-785.10.3926/jiem.1522Search in Google Scholar

21. Seyedkashi, S. M. H., et al. Experimental and Numerical Investigation of an Adaptive Simulated Annealing Technique in Optimization of Warm Tube Hydroforming. – Organization Development Journal, Vol. 22, 2004, pp. 579-583.Search in Google Scholar

22. Malmborg, C. J. A Simulated Annealing Algorithm for Dynamic Document Retrieval. – International Journal of Industrial Engineering, Vol. 10, 2003, No 2, pp. 115-125.Search in Google Scholar

23. Peng, F., G. Cui. Efficient Simultaneous Synthesis for Heat Exchanger Network with Simulated Annealing Algorithm. – Appl. Therm. Eng., Vol. 78, 2015, pp. 136-149.10.1016/j.applthermaleng.2014.12.031Search in Google Scholar

24. Wu, S., G. Wei. High Dimensional Data Clustering Algorithm Based on Sparse Feature Vector for Categorical Attributes. – In: International Conference on Logistics Systems and Intelligent Management, ICLSIM’10, 2010, pp. 973-976.10.1109/ICLSIM.2010.5461099Search in Google Scholar

25. Metropolis, N., A. W. Rosenbluth, M. N. Rosenbluth, A. H. Teller, E. Teller. Equation of State Calculations by Fast Computing Machines. – J. Chem. Phys., Vol. 21, 1953, 1087.Search in Google Scholar

26. Wu, S., D. Jang, Q. Wang. HABOS Clustering Algorithm for Categorical Data. – Chinese Journal of Engineering, Vol. 38, 2016, No 7, pp. 1017-1024.Search in Google Scholar