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An efficient clustering algorithm based on searching popularity peaks
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DOI：10.1007/s10044-024-01261-4.png)
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In order to address some deficiencies of the density peak clustering algorithm, namely sensitivity to density kernels and challenges with large density differences across clusters, we propose a popularity peak clustering algorithm that is based on a more robust notion of density called popularity. The popularity of a sample is computed according to the number, similarity and popularity of points that have the sample in their k-nearest neighbors. The popularity concept has some properties that help in handling challenges like identifying cluster centers in sparse regions and handling situations with large density differences across clusters. Moreover, in the density peak clustering algorithm, the strategy of assigning non-center points to the same cluster as their nearest higher-density neighbor can cause error propagation. To address this issue, we also propose a new popularity-based label assignment strategy. Our results demonstrate that the proposed algorithm can recognize clusters regardless of their densities and overlap degree and can often outperform the existing density peak clustering algorithms.
Keyword:
Density peaks clustering
Label propagation strategy
Popularity peaks clustering
Varying densities
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