International Journal of Computer
Trends and Technology

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Volume 3 | Issue 3 | Year 2012 | Article Id. IJCTT-V3I3P101 | DOI : https://doi.org/10.14445/22312803/IJCTT-V3I3P101

Self Organizing Map based Clustering Approach for Trajectory Data

Sanjiv Kumar Shukla, Sourabh Rungta, Lokesh Kumar Sharma

Citation :

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), vol. 3, no. 3, pp. 311-316, 2012. Crossref, https://doi.org/10.14445/22312803/IJCTT-V3I3P101

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.

Keywords

Trajectory Data, Self-Organizing Map, Clustering.

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