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A local depth based clustering procedure
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DOI：10.1007/s11634-026-00678-5.png)
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Abstract
En 中文
In this paper, we introduce a two-stage partitioning clustering procedure based on local depths. In the first stage, we find clusters of the local depth inner region of level alpha. In the second stage, the remaining points are assigned to one of these clusters according to a proximity criterion. In this way, the clusters found in the first stage play the role of flexible centers, that aim to mimic the shape of the groups. We analyze the performance of the procedure on multivariate and multivariate functional data, on real and synthetic datasets showing remarkable results.
Keywords:
Multivariate functional data
Projection procedures
Depth measures
Outliers
Journal
A
IF:
1.3
Papers:
30
Citations:
883