TCMVS: A Novel Trajectory Clustering Technique Based on Multi-View Similarity

1. Lei, C., M. T. Ozsu, O. Vincent. Robust and Fast Similarity Search for Moving Object Trajectories. - In: Proc. of ACM’2005 SIGMOD International Conf. on Management of Data, Maryland, 2005, pp. 491-502.Search in Google Scholar

2. Hazarath, M., I. Lucio, C. Luca. CAST: A Novel Trajectory Clustering and Visualization Tool for Spatio-Temporal Data. - In: Proc. of 1st International Conference on Intelligent Human Computer Interaction, India, 2009, pp. 169-175.Search in Google Scholar

3. Hazarath, M., M. D. R. M. Sree, J. V. R. Murthy. DenTrac: A Density Based Trajectory Clustering Tool. - International Journal of Computer Applications, Vol. 41, March 2012, No 10, pp. 17-21.10.5120/5576-7674Search in Google Scholar

4. Lee, J., J. Han, K. Whang. Trajectory Clustering: A Partition-and-Group Framework. - In: Proc. of ACM SIGMOD International Conference on Management of Data, Beijing, 2007, pp. 593-604.Search in Google Scholar

5. Agrawal, R., C. Faloutsos, A. Swami. Efficient Similarity Search in Sequence Databases. - In: Proc. of 4th International Conference on Foundations of Data Organization and Algorithms, London, 1993, pp. 69-84.10.1007/3-540-57301-1_5Search in Google Scholar

6. Faloutsos, C., M. Ranganathan, Y. Manolopoulos. Fast Subsequence Matching in Time-Series Databases. - In: Proc. of ACM SIGMOD International Conference on Management of Data, Minnesota, 1994, pp. 419-429.10.1145/191843.191925Search in Google Scholar

7. Keogh, E. J., M. J. Pazzani. Scaling up Dynamic Time Warping for Datamining Applications. - In: Proc. of 6th ACM SIGKDD Intl. Conf. on Knowledge Discovery and Data Mining, 2000, pp. 285-289.10.1145/347090.347153Search in Google Scholar

8. Cynthia, S., F. Dan, R. Daniela. Trajectory Clustering for Motion Prediction. - In: IEEE/RSJ International Conference on Intelligent Robots and Systems, 2012, pp. 1547-1552.Search in Google Scholar

9. Banerjee, A., I. S. Dhillon, J. Ghosh, S. Sra. Clustering on the Unit Hypersphere Using Von Mises-Fisher Distributions. - Journal of Machine Learning Research, Vol. 6, December 2005, pp. 1345-1382.Search in Google Scholar

10. Xu, W., X. Liu, Y. Gong. Document Clustering Based on Non-Negative Matrix Factorization. - In: Proc. of 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Toronto, 2003, pp. 267-273.10.1145/860435.860485Search in Google Scholar

11. Dhillon, I.S., S. Mallela, D. S. Modha. Information-Theoretic Co-Clustering. - In: Proc. of 9th ACM SIGKDD Intl. Conf. on Knowledge Discovery and Data Mining (KDD’03), 2003, pp. 89-98.10.1145/956750.956764Search in Google Scholar

12. Hongyuan, Z., D. Chris, G. Ming, H. Xiaofeng, S. Horst. Spectral Relaxation for K-Means Clustering. - In: Proc. of Neural Info. Processing Systems (NIPS), Vancouver, 2001, pp. 1057-1064.Search in Google Scholar

13. Shi, J., J. Malik. Normalized Cuts and Image Segmentation. - IEEE Trans. Pattern Anal. Mach.Intell., Vol. 22, August 2000, No 8, pp. 888-905.10.1109/34.868688Search in Google Scholar

14. Zhao, Y., G. Karypis. Empirical and Theoretical Comparisons of Selected Criterion Functions for Document Clustering. - Machine Learning, Vol. 55, Jun 2004, No 3, pp. 311-331.10.1023/B:MACH.0000027785.44527.d6Search in Google Scholar

15. Karypis, G. CLUTO a Clustering Toolkit. Technical Report, Dept. of Computer Science, Univ. of Minnesota, 2003. http://glaros.dtc.umn.edu/~gkhome/views/cluto10.21236/ADA439508Search in Google Scholar

16. Strehl, A., J. Ghosh, R. Mooney. Impact of Similarity Measures on Web-Page Clustering. - In: Proc. of 17th Nat’l Conf. Artificial Intelligence: Workshop Artificial Intelligence for Web Search (AAAI’2000), 2000, pp. 58-64.Search in Google Scholar

17. Ahmad, A., L. Dey. A Method to Compute Distance between Two Categorical Values of Same Attribute in Unsupervised Learning for Categorical Data Set. - Pattern Recognition Letters, Vol. 28, 2007, No 1, pp. 110-118.10.1016/j.patrec.2006.06.006Search in Google Scholar

18. Ienco, D., R. G. Pensa, R. Meo. Context-Based Distance Learning for Categorical Data Clustering. - In: Proc. of 8th Int’l Symp. Intelligent Data Analysis (IDA), 2009, pp. 83-94.10.1007/978-3-642-03915-7_8Search in Google Scholar

19. Nguyen, D. T., L. Chen, C. K. Chan. Clustering with Multi-Viewpoint Based Similarity Measure. - IEEE Transactions on Knowledge and Data Engineering, 2011, pp. 1-15.Search in Google Scholar

20. Prasad, C. V. V. D., M. H. M. Krishna Prasad, V. V. Bhaskar. Evaluation of Multi- Viewpoint Similarity Based Document Clustering. - In: Proc. of 3rd International Conference on Recent Trends in Engineering & Technology (ICRTET’2014), 2014.Search in Google Scholar

21. Abbas, N. Graph Clustering: Complexity, Sequential and Parallel Algorithms. Phd Thesis, University of Alberta, Edmonton, 1995.Search in Google Scholar

22. Xiang, W., Q. Buyue, Y. Jieping, D. Ian. Multi-Objective Multi-View Spectral Clustering via Pareto Optimization. - In: Proc. of 13th SIAM International Conference on Data Mining, Austin, 2013, pp. 234-242.10.1137/1.9781611972832.26Search in Google Scholar

23. Rand, M. William. Objective Criteria for the Evaluation of Clustering Methods. - Journal of the American Statistical Association, Vol. 66, 1971, No 336, pp. 846-850.10.1080/01621459.1971.10482356Search in Google Scholar

24. M. Halkidi, M., Y. Batistakis, M. Vazirgiannis. On Clustering Validation Techniques. - Journal of Intelligent Information Systems, Vol. 17, December 2001, No 2, pp. 107-145.10.1023/A:1012801612483Search in Google Scholar

25. Fowlkes, E. B., C. L. Mallows. A Method for Comparing Two Hierarchical Clusterings. - Journal of the American Statistical Association, Vol. 78, September 1983, No 383.10.2307/2288123Search in Google Scholar

26. T-Drive Trajectory’s Dataset (downloaded on 8 September 2013). http://research.microsoft.com/apps/pubs/?id=152883Search in Google Scholar

27. GeoLife Trajectory’s (downloaded on 23 September 2013). http://research.microsoft.com/en-us/downloads/b16d359d-d164-469e-9fd4-daa38f2b2e13/Search in Google Scholar