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Self Organizing Map based Clustering Approach for Trajectory Data

  IJCOT-book-cover
 
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.

MLA

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.

References-

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KeywordsTrajectory Data, Self-Organizing Map, Clustering.

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