Abstract
This paper discusses an evolutionary clustering algorithm that uses dynamic representative points as the core of sample cluster (DRPEC). DRPEC algorithm calculates the similarity between samples and representative points by Gaussian function, and splits the winning representative points according to the principle of “Winner-take-all”, to construct a representative point spanning tree that can represent the cluster relation. DRPEC algorithm takes a representative point as the starting point, realizes the dynamic evolution of the representative points in an incremental way, and merges the sub-clusters represented by the nodes in the representative point spanning tree according to our designed measuring function, in order to effectively find the natural clusters existing in the data space. Finally, numerous experiments were conducted on UCI datasets, and compared with the current popular clustering algorithm, the results show that the DRPEC algorithm has excellent clustering performance and strong robustness.
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Li, P., Xie, H., Ding, Z. (2021). DRPEC: An Evolutionary Clustering Algorithm Based on Dynamic Representative Points. In: Fang, L., Chen, Y., Zhai, G., Wang, J., Wang, R., Dong, W. (eds) Artificial Intelligence. CICAI 2021. Lecture Notes in Computer Science(), vol 13069. Springer, Cham. https://doi.org/10.1007/978-3-030-93046-2_64
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DOI: https://doi.org/10.1007/978-3-030-93046-2_64
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