Multiple source clustering: A probabilistic reasoning approach

In this paper we describe a versatile Multiple Source Clustering (MSC) algorithm. The algorithm uses a form of probabilistic reasoning known as Bayesian networks to solve the MSC problem of incomparable feature spaces. For time-tagged data, the algorithm uses fuzzy conjunctions to support cluster formation and management. Clustering performance measures are defined and a multiple target tracking/multiple sensor example is presented.