  The hidden Markov model (HMM) is a generative model that treats sequential
data under the assumption that each observation is conditioned on the state of
a discrete hidden variable that evolves in time as a Markov chain. In this
paper, we derive a novel algorithm to cluster HMMs through their probability
distributions. We propose a hierarchical EM algorithm that i) clusters a given
collection of HMMs into groups of HMMs that are similar, in terms of the
distributions they represent, and ii) characterizes each group by a "cluster
center", i.e., a novel HMM that is representative for the group. We present
several empirical studies that illustrate the benefits of the proposed
algorithm.
