Abstract
The problem is to predict a value y∊ Y (output, class) from an observed value of a vector x ∊ X (predictors, inputs, attributes), the relations between y and x given in (empirical) data D − {(x i , y i ): i − 1,…,N}, listing N observed pairs. We propose an estimation algorithm using a classification of D in clusters {Ω 1 ,….,Ω m }, based on a distance function in X× Y. For each cluster Ω i compute the centroid y̅ i of y, and denote me X — projection of Ω i by Ω x i . Prediction of y given x ∊ X is done by assigning the point x to a nearest projected cluster, say Ω x i , and using y̅i as estimate for y. Numerical tests show the method, in its basic general form, to give accurate predictions for well-known data sets.
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Ben-Israel, A., Levin, Y. An Estimation Algorithm using Distance Clustering of Data. OPSEARCH 38, 443–455 (2001). https://doi.org/10.1007/BF03398650
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DOI: https://doi.org/10.1007/BF03398650