Abstract
An accurate data characterization is essential for a reliable selection of clustering algorithms via meta-learning. This work evaluates a set of measures for characterizing clustering problems using beta regression and two well-known machine learning regression techniques as meta-models. We have observed a subset of meta-features which demonstrates greater resourcefulness to characterize the clustering datasets. In addition, secondary findings made it possible to verify the direction and magnitude of the influence and the importance of such measures in predicting the performance of the algorithms under analysis.
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To the Brazilian research agency CNPq.
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Fernandes, L.H.d.S., de Souto, M.C.P., Lorena, A.C. (2021). Evaluating Data Characterization Measures for Clustering Problems in Meta-learning. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Lecture Notes in Computer Science(), vol 13108. Springer, Cham. https://doi.org/10.1007/978-3-030-92185-9_51
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