Research Article Open Access

An Efficient Algorithm for Mining Spatially Co-located Moving Objects

G. Manikandan1 and S. Srinivasan2
  • 1 Department of Computer Science and Engineering, Sathyabama University, Chennai, India
  • 2 Department of Computer Science and Engineering, Anna University of Technology Madurai,, India

Abstract

Mining co-location patterns from spatial databases may disclose the types of spatial features which are likely located as neighbors’ in space. Accordingly, we present an algorithm previously for mining spatially co-located moving objects using spatial data mining techniques and Prim’s Algorithm. In the previous technique, the scanning of database to mine the spatial co-location patterns took much computational cost. In order to reduce the computation time, in this study, we make use of R-tree that is spatial data structure to mine the spatial co-location patterns. The important step presented in the approach is that the transformation of spatial data into the compact format that is well-suitable to mine the patterns. Here, we have adapted the R-tree structure that converts the spatial data with the feature into the transactional data format. Then, the prominent pattern mining algorithm, FP growth is used to mine the spatial co-location patterns from the converted format of data. Finally, the performance of the proposed technique is compared with the previous technique in terms of time and memory usage. From the results, we can ensure that the proposed technique outperformed of about more than 50% of previous algorithm in time and memory usage.

American Journal of Applied Sciences
Volume 10 No. 3, 2013, 195-208

DOI: https://doi.org/10.3844/ajassp.2013.195.208

Submitted On: 24 April 2012 Published On: 2 March 2013

How to Cite: Manikandan, G. & Srinivasan, S. (2013). An Efficient Algorithm for Mining Spatially Co-located Moving Objects. American Journal of Applied Sciences, 10(3), 195-208. https://doi.org/10.3844/ajassp.2013.195.208

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Keywords

  • Spatial Data Mining
  • Co-Location Patterns
  • Minimum Support
  • Minimum Bounding Rectangle
  • FP Tree
  • Vehicle Movement Data