The week's pick
Random Articles
Reseach Article
DenTrac: A Density based Trajectory Clustering Tool
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 41 - Number 10 |
| Year of Publication: 2012 |
| Authors: Hazarath Munaga, M. D. R. Mounica Sree, J. V. R. Murthy |
10.5120/5576-7674
|
Hazarath Munaga, M. D. R. Mounica Sree, J. V. R. Murthy . DenTrac: A Density based Trajectory Clustering Tool. International Journal of Computer Applications. 41, 10 ( March 2012), 17-21. DOI=10.5120/5576-7674
Abstract
In this paper, we present a novel density based trajectory clustering technique for clustering and visualizing Spatio-temporal data to analyze the navigational behavior of moving entities, such as users, virtual characters or vehicles. For testing our proposal, we developed DenTrac (Density based Trajectory Clustering and visualization tool for Spatio-Temporal data), a tool designed to analyze the moving entities navigating in real as well as virtual environments. Such analysis allows the analyst to derive the information at a level of abstraction suitable to support (i) the evaluation of user spaces and (ii) the identification of the predominant navigation behavior of users. We demonstrate the effectiveness of our solution by testing the tool on data acquired by recording the movements of users navigating through a virtual environment.
References
- Ganey, S. , Smyth, P. : Trajectory clustering with mixtures of regression models. In: KDD '99: Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining, New York, NY, USA, ACM (1999) 63?72.
- Phang, T. L. , Neville, M. C. , Rudolph, M. , Hunter, L. , Trajectory clustering: A non-parametric method for grouping gene expression time. In: Proceedings of Pacific Symposium on Biocomputing, Courses, With Applications to Mammary Development. (2003) 8?351.
- Sas, C. , O'Hare, G. , Reilly, R. : Virtual environment trajectory analysis: a basis for navigational assistance and scene adaptivity. Future Gener. Comput. Syst. 21(7) (2005) 1157?1166.
- Chittaro, L. , Ranon, R. , Ieronutti, L. : Vu-flow: A visualization tool for analyzing navigation in virtual environments. IEEE Transactions on Visualization and Computer Graphics 12(6) (2006) 1475?1485.
- Hazarath, M. , Lucio, I. , Luca, C. : CAST - A novel trajectory clustering and visualization tool for spatio temporal data. In: IHCI-2009: Proceedings of the First International conference on Intelligent Human Computer Interaction, Springer (2009) 169?175.
- Hazarath, M. , Murthy, J. V. R. , Venkateswarlu, N. B. : A Hybrid Trajectory Clustering for Predicting User Navigation. International Journal on Recent Trends in Engineering 3(1) (2010) 76?80.
- Kriegel, H. P. , Kunath, P. , Pfeifle, M. , Renz, M. : Viewnet: Visual exploration of region-wide traffic networks. In: ICDE. (2006) 166.
- Tan, P. N. , Steinbach, M. , Kumar, V. : Introduction to Data Mining. Addison-Wesley (2005).
- Hazarath, M. , Murthy, J. V. R. , Venkateswarlu, N. B. : A Novel Trajectory clustering technique for selecting cluster heads in Wireless sensor networks. International Journal on Recent Trends in Engineering 1(1) (2009) 357?361.
- Agrawal, R. , Faloutsos, C. , Swami, A. N. : Efficient similarity search in sequence databases. In: FODO '93: Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms, London, UK, Springer-Verlag (1993) 69?84.
- Faloutsos, C. , Ranganathan, M. , Manolopoulos, Y. : Fast subsequence matching in time-series databases. In: SIGMOD '94: Proceedings of the 1994 ACM SIGMOD international conference on Management of data, New York, NY, USA, ACM (1994) 419?429.
- Laurinen, P. , Siirtola, P. , R"oning, J. : Efficient algorithm for calculating similarity between trajectories containing an increasing dimension. In: AIA'06: Proceedings of the 24th IASTED international conference on Artificial intelligence and applications, Anaheim, CA, USA, ACTA Press (2006) 392?399.
- Ester, M. , Kriegel, H. P. , Sander, J. , Xu, X. : A density-based algorithm for discovering clusters in large spatial databases with noise. In: KDD. (1996) 226?231.
- Hinneburg, A. , Hinneburg, E. , Keim, D. A. : An efficient approach to clustering in large multimedia databases with noise. In: KDD'98, AAAI Press (1998) 58?65.
- Chiu, T. , Fang, D. , Chen, J. , Wang, Y. , Jeris, C. : A robust and scalable clustering algorithm for mixed type attributes in large database environment. In: KDD '01: Proceedings of the 7th ACM SIGKDD international conference on Knowledge discovery and data mining, New York, NY, USA, ACM (2001) 263?268.
- Foss, A. , Zaïane, O. R. : A parameter less method for efficiently discovering clusters of arbitrary shape in large datasets. In: ICDM '02: Proceedings of the 2002 IEEE International Conference on Data Mining, Washington, DC, USA, IEEE Computer Society (2002) 179.
- Harris, R. , Hess, D. , Venegas, J. : An objective analysis of the pressure-volume curve in the acute respiratory distress syndrome. American Journal of Respiratory and Critical Care Medicine 161(2) (February 2000) 432?439.
- Zhao, Q. , Hautamaki, V. , Fränti, P. : Knee point detection in bic for detecting the number of clusters. In: ACIVS '08: Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems, Berlin, Heidelberg, Springer-Verlag (2008) 664?673
Index Terms
Keywords