Geodesic
Clustering algorithm based on feature space partitioning
Vestnik KRAUNC. Fiziko-matematičeskie nauki, Tome 39 (2022) no. 2, pp. 136-149

Voir la notice de l'article provenant de la source Math-Net.Ru

A new approach to robust clustering is proposed based on recursive partitioning of the feature space and density analysis. An algorithm for robust clustering of linearly inseparable points, its software implementation, as well as test results on classical data distributions are presented.
Keywords: clustering, robust clustering, machine learning. 
@article{VKAM_2022_39_2_a9,
     author = {M. A. Kazakov},
     title = {Clustering algorithm based on feature space partitioning},
     journal = {Vestnik KRAUNC. Fiziko-matemati\v{c}eskie nauki},
     pages = {136--149},
     publisher = {mathdoc},
     volume = {39},
     number = {2},
     year = {2022},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/VKAM_2022_39_2_a9/}
}
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M. A. Kazakov. Clustering algorithm based on feature space partitioning. Vestnik KRAUNC. Fiziko-matematičeskie nauki, Tome 39 (2022) no. 2, pp. 136-149. http://geodesic.mathdoc.fr/item/VKAM_2022_39_2_a9/