Cluster detection in background noise

Abstract If a feature space contains a set of clusters and background noise, it may be difficult to extract the clusters correctly. In particular, when we use a partitioning scheme such as k-means clustering, where k is the correct number of clusters, the background noise points are forced to join the clusters, thus biasing their statistics. This paper describes a preprocessing technique that gives each data point a weight related to the density of data points in its vicinity. Points belonging to clusters thus get relatively high weights, while background noise points get relatively low weights. k-means clustering of the resulting weighted points converges faster and yields more accurate clusters.