Abstract
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.
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Notes
- 1.
This phenomenon is most likely related to properties of a specific CVI and can be further mitigated, e.g. by applying different initialization method or using a more complex mutation/evolutionary scheme.
- 2.
Full collection of comparison boxplots can be found at https://bit.ly/2Zr3WwG.
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Acknowledgments
The authors would like to thank Maxim Buzdalov for useful comments. The research was financially supported by The Russian Science Foundation, Agreement 17-71-30029.
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Tomp, D., Muravyov, S., Filchenkov, A., Parfenov, V. (2019). Meta-learning Based Evolutionary 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_54
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