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

Open access

Abstract

The analysis of moving entities “trajectories” is an important task in different application domains, since it enables the analyst to design, evaluate and optimize navigation spaces. Trajectory clustering is aimed at identifying the objects moving in similar paths and it helps the analysis and obtaining of efficient patterns. Since clustering depends mainly on similarity, the computing similarity between trajectories is an equally important task. For defining the similarity between two trajectories, one needs to consider both the movement and the speed (i.e., the location and time) of the objects, along with the semantic features that may vary. Traditional similarity measures are based on a single viewpoint that cannot explore novel possibilities. Hence, this paper proposes a novel approach, i.e., multi viewpoint similarity measure for clustering trajectories and presents “Trajectory Clustering Based on Multi View Similarity” technique for clustering. The authors have demonstrated the efficiency of the proposed technique by developing Java based tool, called TCMVS and have experimented on real datasets.

If the inline PDF is not rendering correctly, you can download the PDF file here.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 15. Karypis G. CLUTO a Clustering Toolkit. Technical Report Dept. of Computer Science Univ. of Minnesota 2003. http://glaros.dtc.umn.edu/~gkhome/views/cluto

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 21. Abbas N. Graph Clustering: Complexity Sequential and Parallel Algorithms. Phd Thesis University of Alberta Edmonton 1995.

  • 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.

  • 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.

  • 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.

  • 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.

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

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

Search
Journal information
Impact Factor


CiteScore 2018: 0.84

SCImago Journal Rank (SJR) 2018: 0.215
Source Normalized Impact per Paper (SNIP) 2018: 0.595

Mathematical Citation Quotient (MCQ) 2017: 0.01

Metrics
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 174 100 1
PDF Downloads 61 46 2