A general probabilistic framework for clustering individuals and objects

This paper presents a unifying probabilistic framework for clustering individuals or systems into groups when the available data measurements are not multivariate vectors of xed dimensionality. For example, one might have data from a set of medical patients, where for each patient one has a set of of observed time-series, each time-series of potentially dierent length and dierent sampling rate. We propose a general model-based probabilistic framework for clustering data types of this form which are non-vector in nature and may vary in size from individual to individual. The Expectation-Maximization (EM) procedure for clustering within this framework is discussed and we discuss how it be applied in a general manner to clustering of sequences, time-series, trajectories, and other non-vector data. We show that a number of earlier algorithms can be viewed as special cases within this unifying framework. The paper concludes with several illustrations of the method, including clustering o...