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DENCAST: distributed density-based clustering for multi-target regression

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Fig. 1 Overview of the environment in which DENCAST works. In the figure, data coming from multiple sources are fed into DENCAST, which produces a clustering model that is exploited for predictive purposes on new data (single-target or multi-target regress
Fig. 2 Workflow of the proposed method. Orange blocks represent inputs (labeled and unlabeled objects) and outputs (predictions); grey blocks represent intermediate results; blue blocks represent the different phases of the proposed method
Fig. 3 Graphic representation of the clustering approach. propagate their cluster ID to their neighbors (messages
Fig. 4 Graphical representation of the prediction phase of our approach. Each unlabeled object is assigned to the cluster containing the most similar labeled object (dotted lines) and the value of its target attributes are predicted according to a weighted average of the values assumed by the labeled objects in such a cluster (crossed circle)
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