We consider the problem of clustering data sets where the data points are dynamic, or essentially time-varying. Our approach is to incorporate features of both the deterministic annealing algorithm as well as control theoretic methods in our computational solution. Extensions of our method can be made to the problem of aggregating time-varying graphs, for which we have developed a quantitative measure of dissimilarity that allows us to compare directed graphs of differing sizes. In this talk, an overview of our dynamic clustering algorithm will be given, along with some analysis of the algorithm properties. We will conclude with a few highlighted applications, and further extensions as time allows.
