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This paper further extends the 'kernel'-based approach to clustering proposed by E. Diday in early 70s. According to this approach, a cluster's centroid can be represented by parameters of any analytical model, such as linear regression equation, built over the cluster. We address the problem of producing regressionwise clusters to be separated in the input variable space by building a hybrid clustering criterion that combines the regression-wise clustering criterion with the conventionaldoi:10.1007/978-3-540-73560-1_21 fatcat:bxjhha7oqbhbbfdpzoyxxv3zv4