In this work, we address the hard clustering problem. We present a new clustering algorithm based on evolutionary computation searching a best partition with respect to a given quality measure. We present 32 partition transformation that are used as mutation operators. The algorithm is a (1 + 1 ) evolutionary strategy that selects a random mutation on each step from a subset of preselected mutation operators. Such selection is performed with a classifier trained to predict usefulness of each mutation for a given dataset. Comparison with state-of-the-art approach for automated clustering algorithm and hyperparameter selection shows the superiority of the proposed algorithm.
CITATION STYLE
Tomp, D., Muravyov, S., Filchenkov, A., & Parfenov, V. (2019). Meta-learning based evolutionary clustering algorithm. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11871 LNCS, pp. 502–513). Springer. https://doi.org/10.1007/978-3-030-33607-3_54
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