Flexible grid-based clustering

Abstract

Grid-based clustering is particularly appropriate to deal with massive datasets. The principle is to first summarize the dataset with a grid representation, and then to merge grid cells in order to obtain clusters. All previous methods use grids with hyper-rectangular cells. In this paper we propose a flexible grid built from arbitrary shaped polyhedra for the data summary. For the clustering step, a graph is then extracted from this representation. Its edges are weighted by combining density and spatial informations. The clusters are identified as the main connected components of this graph. We present experiments indicating that our grid often leads to better results than an adaptive rectangular grid method. © Springer-Verlag Berlin Heidelberg 2007.