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- W4313124590 abstract "Density peak clustering (DPC) defines cluster centers to be the objects with the highest density in their neighborhoods and far away from the objects that are with higher densities, where density of each object is determined by a pre-specified parameter. This definition may find the number of clusters and detect clusters with arbitrary shapes. However, as a drawback, it may also lead to multiple centers for one cluster and hence degrade the clustering quality. Some work in the literature try to remedy it, but this drawback can be regarded as the advantage in split-and-merge strategy-based clustering. In this paper, we make use of a modified DPC to partition a dataset into a large number of small clusters by defining a refined K nearest neighbors, and design a merge criterion to combine the small clusters. The experimental results on 6 synthetic and 6 real datasets demonstrate the proposed algorithm outperforms some traditional clustering algorithms." @default.
- W4313124590 created "2023-01-06" @default.
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- W4313124590 creator A5037518553 @default.
- W4313124590 date "2022-01-01" @default.
- W4313124590 modified "2023-10-16" @default.
- W4313124590 title "Density Peak Clustering Based Split-and-Merge" @default.
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- W4313124590 doi "https://doi.org/10.1007/978-3-031-21244-4_14" @default.
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