Abstract

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 is based on well known spatial data structures, namely the range tree, that allows fast range searches. From a theoretical standpoint, the k- windows algorithm has a lower time complexity than the other well-known existing clustering algorithms. Moreover, it achieves high quality clustering results. However,it seems that it would not be directly applicable in high-dimensional practical settings due to the superlinear space requirements for the range tree. In this paper we present an improvement of the k-windows algorithm, aiming at attacking this problem, that it is based on a different solution to the orthogonal range search problem.

Citations