CUZ: An Improved Clustering Algorithm

Clustering is for many years now one of the most complex and most studied problems in data mining. Until now the most commonly used algorithm for finding groups of similar objects in large databases is CURE. The main advantage of CURE, compared to other clustering algorithms, is its ability to identify non spherical or rectangular shaped objects. In this paper we present a new algorithm called CUZ (Clustering Using Zones). The main innovation of CUZ lies in the technique that it uses to calculate the representatives. This technique overcomes the problem of identifying clusters with non-convex shapes. Experimental results show that CUZ is a generally competitive technique, while it is particularly adequate when we have to do with clusters that do not have convex shapes.