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In data mining, cluster-weighted modeling is an algorithm-based approach to non-linear prediction of outputs from inputs based on density estimation using a set of models that are each notionally appropriate in a sub-region of the input space. The overall approach works in jointly input-output space and an initial version was proposed by Neil Gershenfeld.