Abstract

Spatial Data Mining is based on correlation of spatial objects in space. Mining frequent pattern from spatial databases systems has always remained a challenge for researchers. In the light of the first law of geography “everything is related to everything else but nearby things is more related than distant things” suggests that values taken from samples of spatial data near to each other tend to be more similar than those taken farther apart. This tendency is termed spatial autocorrelation or spatial dependence. It’s natural that most spatial data are not independent, they have high autocorrelation. In this paper, we propose an enhancement of existing mining algorithm for efficiently mining frequent patterns for spatial objects occurring in space such as a city is located near a river. The frequency of each spatial object in relation to other object tends to determine multiple occurrence of the same object. We further enhance the proposed approach by using a numerical method. This method uses a tree structure based methodology for mining frequent patterns considering the frequency of each object stored at each node of the tree. Experimental results suggest significant improvement in finding valid frequent patterns over existing methods.

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