Self Organizing Map based Clustering Approach for Trajectory Data
| ||International Journal of Computer Trends and Technology (IJCTT)|| |
|© - Issue 2012 by IJCTT Journal|
|Volume-3 Issue-3 |
|Year of Publication : 2012|
|Authors :Sanjiv Kumar Shukla, Sourabh Rungta, Lokesh Kumar Sharma.|
Sanjiv Kumar Shukla, Sourabh Rungta, Lokesh Kumar Sharma."Self Organizing Map based Clustering Approach for Trajectory Data"International Journal of Computer Trends and Technology (IJCTT),V3(3):311-316 Issue 2012 .ISSN 2231-2803.www.ijcttjournal.org. Published by Seventh Sense Research Group.
Abstract: -Clustering algorithm for the moving or trajectory data provides new and helpful information. It has wide application on various location aware services. In this study the Self Organizing Map is used to form the cluster on trajectory data. The self-organizing map (SOM) is an important tool in exploratory phase of data mining. It projects input space on prototypes of a low-dimensional regular grid that can be effectively utilized to visualize and explore properties of the data. When the number of SOM units is large, to facilitate quantitative analysis of the map and the data, similar units need to be grouped, i.e., clustered.
 A. K. Jain, M. N. Murty, P. J .Flynn, “ Data Clustering: A Review.” ACM Computing Surveys, Vol. 31, No. 3, pp. 265-323, Sep. 1999.
 A. Akasapu et al. “Density Based k-Nearest Neighbors Clustering Algorithm for Trajectory Data”, Int. J. on Advanced Science and Technology, Vol. 31, June 2011, pp. 47-57, 2011.
 F. Giannotti and D. Pedreschi, “Mobility, Data Mining and Privacy: Geographic Knowledge Discovery”, Springer Verlag, 2008.
 F. Ginnotti, M. Nanni, D. Pedreschi and F. Pinelli, “Trajectory Pattern Mining”, In Proceedings of the 13th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, pp. 330 – 339, 2007.
 G. Andrienko, N. Andrienko, and S. Wrobel, “Visual Analytics Tools for Analysis of Movement Data”, ACM SIGKDD: 38-46, ISSN:1931- 0145, 2007.
 G. Massini, "Applications of Mathematics in Models, Artificial Neural Networks and Arts", Chapter 13, Springer, 2010.
 J. Gudmundsson, P. Laube and T. Wolle T. “Movement Patterns in Spatio-Temporal Data”, In: Shekhar, S. & Xiong, H. (eds.). Encyclopedia of GIS, Springer-Verlag, 2008.
KeywordsTrajectory Data, Self-Organizing Map, Clustering.