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 present an algorithm that directly finds a set of cluster centers based on an analysis of the distribution of patterns in the local neighborhood of each potential cluster 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 cluster independently. We demonstrate how the algorithm works on an artificial data set and show its usefulness using a well-known benchmark data set