  Different spatial objects that vary in their characteristics, such as
molecular biology and geography, are presented in spatial areas. Methods to
organize, manage, and maintain those objects in a structured manner are
required. Data mining raised different techniques to overcome these
requirements. There are many major tasks of data mining, but the mostly used
task is clustering. Data set within the same cluster share common features that
give each cluster its characteristics. In this paper, an implementation of
Approximate kNN-based spatial clustering algorithm using the K-d tree is
proposed. The major contribution achieved by this research is the use of the
k-d tree data structure for spatial clustering, and comparing its performance
to the brute-force approach. The results of the work performed in this paper
revealed better performance using the k-d tree, compared to the traditional
brute-force approach.
