A nearest-neighbor method as a data preparation tool for a clustering genetic algorithm

Eduardo R. HruschkaEstevam R. Hruschka Jr.Nelson F. F. Ebecken

This paper presents a Nearest-Neighbor Method to substitute missing values in continuous datasets and show that it can be useful for a Clustering Genetic Algorithm. The proposed method is evaluated by means of simulations performed in the Wisconsin Breast Cancer Dataset, which is a benchmark for data mining methods. In this sense, we verify the efficacy of the proposed method in the context of a Clustering Genetic Algorithm, comparing the average classification rates obtained in the original dataset with those obtained in a dataset formed by the substituted values. The simulation results show that the proposed method is promising.

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