Abstract
The mixed data clustering algorithms have been timidly emerging since the end of the last century. One of the last algorithms proposed for this data-type has been KAMILA (KAy-means for MIxed LArge data) algorithm. While the KAMILA has outperformed the previous mixed data algorithms results, it has some gaps. Among them is the definition of numerical and categorical variable weights, which is a user-defined parameter or, by default, equal to one for all features. Hence, we propose an optimization algorithm called Biased Random-Key Genetic Algorithm for Features Weighting (BRKGAFW) to accomplish the weighting of the numerical and categorical variables in the KAMILA algorithm. The experiment relied on six real-world mixed data sets and two baseline algorithms to perform the comparison, which are the KAMILA with default weight definition, and the KAMILA with weight definition done by the traditional genetic algorithm. The results have revealed the proposed algorithm overperformed the baseline algorithms results in all data sets.
This work was supported by the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) - Brazil under grant number 306075/2017-2 and 430137/2018-4 and by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) - Brazil.
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Martarelli, N.J., Nagano, M.S. (2019). Optimization of the Numeric and Categorical Attribute Weights in KAMILA Mixed Data Clustering Algorithm. In: Yin, H., Camacho, D., Tino, P., Tallón-Ballesteros, A., Menezes, R., Allmendinger, R. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2019. IDEAL 2019. Lecture Notes in Computer Science(), vol 11871. Springer, Cham. https://doi.org/10.1007/978-3-030-33607-3_3
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