Multiple source clustering: a probabilistic reasoning approach
T.J. Leih, Jørgen E. Harmse, Evangelos Giannopoulos · 2002
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.