A Parallel Approach for Finding Spatial Colocation Patterns

Abstract

Spatial data mining come to be one of the vital essential areas due to the fact that of the fast evolution in science which leads in enormous spatial data.Co-location data mining is an interesting and most important predicament in spatial data mining discipline which discovers the subsets of points whose events are usually placed together in geographic area.Spatial proximity is the major inspiration to verify the colocation patterns from huge data.The computation of co-location data discovery is very high-priced with enormous data volume and nearby existence of neighborhoods. So there is number of spatial co-loaction mining algorithms had been developed to overcome these drawbacks. In this paper, a new co-location data mining framework has been proposed that benefits from the power of parallel processing,in particular, the Map Reduce to obtain higher spatial mining processing effectivity. Map Reduce model have been proven to be an efficient framework solution for big dataprocessing on clusters of commodity machines, and for big data evaluation and lots of functions. The experimental result of the proposed framework indicates scalable and efficient computational efficiency.