Similarity based clustering using the expectation maximization algorithm
In this paper we present a new approach for clustering data. The clustering metric used is the normalized cross-correlation, also known as similarity, instead of the traditionally used Euclidean distance. The main advantage of this metric is that it depends on the signal shape rather than its amplitude. Under an assumption of an exponential probability model that has several desirable properties, the expectation-maximization (EM) framework is used to derive two iterative clustering algorithms. Numerical experiments are presented using simulated data in a dynamic positron emission topography study of the brain. Initial results demonstrate that the proposed method achieves better performance than several existing clustering methods.