%0 Journal Article
%T Approximate K-Nearest Neighbour Based Spatial Clustering Using K-D Tree
%A Mohammed Otair
%J International Journal of Database Management Systems
%D 2013
%I Academy & Industry Research Collaboration Center (AIRCC)
%X Different spatial objects that vary in their characteristics, such as molecular biology and geography, arepresented in spatial areas. Methods to organize, manage, and maintain those objects in a structuredmanner are required. Data mining raised different techniques to overcome these requirements. There aremany major tasks of data mining, but the mostly used task is clustering. Data set within the same clustershare common features that give each cluster its characteristics. In this paper, an implementation ofApproximate kNN-based spatial clustering algorithm using the K-d tree is proposed. The majorcontribution achieved by this research is the use of the k-d tree data structure for spatial clustering, andcomparing its performance to the brute-force approach. The results of the work performed in this paperrevealed better performance using the k-d tree, compared to the traditional brute-force approach.
%K Spatial data
%K Spatial Clustering
%K Approximate kNN
%K K-d tree
%K brute-force.
%U http://airccse.org/journal/ijdms/papers/5113ijdms08.pdf