Determining the Number of Clusters with Rate-Distortion Curve Modeling

In this paper we consider a problem of an unsupervised clustering of multidimensional numerical data. We propose a new method for determining an optimal number of clusters in a data set which is based on a parametric model of a Rate-Distortion curve. The proposed method can be used in conjunction with any suitable clustering algorithm. It was tested with artificial and real numerical data sets and the results of experiments demonstrate empirically not only effectiveness of the method but also its ability to cope with “difficult” cases where other known methods failed.