A Simple Clustering Algorithm Based on Weighted Expected Distances

This paper contains a proposal to assign points to clusters, represented by their centers, based on weighted expected distances in a cluster analysis context. The proposed clustering algorithm has mechanisms to create new clusters, to merge two nearby clusters and remove very small clusters, and to identify points `noise' when they are beyond a reasonable neighborhood of a center or belong to a cluster with very few points. The presented clustering algorithm is evaluated using four randomly generated and two well-known data sets. The obtained clustering is compared to other clustering algorithms through the visualization of the clustering, the value of the DB validity measure and the value of the sum of within-cluster distances. The preliminary comparison of results shows that the proposed clustering algorithm is very efficient and effective.