This paper proposes a scheme called Augmented Model Visualization for Data Mining (AMV-DM), based on models of visual perception and interaction to represent and operate with data mining models. The scheme has at its core the use of complementary visualizations applied to data-mining (DM) model during the adjustment phase. These complementary views correspond to: a second descriptive technique of data mining, and an appropriate set of graphical artifacts. Defined metrics that measure the distance and similarity of components of a model and allow visual perception empirically data-analyst. AMV-DM is implemented through a prototype visual environment. As a case study explores a decision tree model and each of its nodes. Apply Self-Organizing Map technique on the decision tree (DT) model with a set of graphical artifacts. Two controlled experiments were carried out with 30 users. Preliminary results analysis allows obtaining empirical evidence of the usefulness of the proposed scheme. (C) 2015 Published by Elsevier B.V.