Regression trees are helpful tools for knowledge discovery and decision support, due to their simple structure and the easiness to obtain them from data. Nonetheless, when applied to non-trivial datasets, they tend to grow according to the complexity of the data, becoming uneasy to interpret. In this work, we propose a clustering perturbation method to reduce the size of the regression tree obtained from each cluster. A prototype has been developed and tested on several regression datasets.
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