Abstract
Missing values are a critical problem in data mining applications. The substitution of these values, also called imputation, can be performed by several methods. This work describes the application of an optimized version of the Bayesian Algorithm K2 as an imputation tool for a clustering genetic algorithm. The resulting hybrid system is assessed by means of simulations in five benchmark datasets. The obtained results indicate that the proposed imputation method is a suitable data preparation tool for the employed clustering genetic algorithm.
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