Abstract In recent years, there has been a growing interest in clustering uncertain objects. In contrast to traditional, `sharp' data representation models, uncertain objects are modeled as probability distributions defined over uncertainty regions. In this context, a major issue is related to the poor efficiency of existing algorithms, which is mainly due to expensive computation of the distance between uncertain objects. In this work, we extend our earlier work in which a novel formulation to.
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