Improving the orthogonal range search k-windows algorithm

Clustering, that is the partitioning of a set of patterns into disjoint and homogeneous meaningful groups (clusters), is a fundamental process in the practice of science. k-windows is an efficient clustering algorithm that reduces the number of patterns that need to be examined for similarity. using a windowing technique. It exploits well known spatial data structures, namely the range free, that allows fast range searches. From a theoretical standpoint, the k-windows algorithm is characterized by lower time complexity compared to other well-known clustering algorithms. Moreover it achieves high quality clustering results. However, it appears that it cannot be directly applicable in high-dimensional settings due to the superlinear space requirements for the range tree. In this paper an improvement of the k-windows algorithm, aiming at resolving this deficiency, is presented. The improvement is based on an alternative solution to the orthogonal range search problem.