Finding Clusters in Parallel Universes

Many clustering algorithms have been proposed in recent years. Most methods operate in an iterative manner and aim to optimize a specific energy function. We pres~nt an algorithm that directly find., a set of C1l1Ster centers based on an analysis of the distribution of patterns in the local neighborhood of each potential duster center through the use of so-called Neighborgrams. In addition this analysis can be carried out in several feature spaces in parallel, effectively finding the optimal set of features for each duster independently. We denlOnstrate how the algorithm works on an artificial data set and show its usefulness using a well-known benchmark data set.