Abstract
As a promising clustering approach, the density peak (DP) based algorithm utilizes the data density and carefully designed distance to identify cluster centers and cluster members. The key to this approach is the density calculation, which has a significant impact on the clustering results. However, the original DP algorithm applies the local density to identify cluster centers directly, and fails to take into account the density difference among clusters. As a result, large-density clusters may be partitioned into multiple parts and small-density clusters are likely to be merged with other clusters. In this paper we introduce a density normalization step to deal with this problem, and show that the normalized density can be used to characterize cluster centers more accurately than the original one. In experiments on various datasets, our method is shown to improve the performance of different density kernels evidently.
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Acknowledgement
This work is supported in part by National Natural Science Foundation of China under Grant No. 61473045 and No. 41371425, and in part by China Scholarship Council.
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Hou, J., Cui, H. (2017). Density Normalization in Density Peak Based Clustering. In: Foggia, P., Liu, CL., Vento, M. (eds) Graph-Based Representations in Pattern Recognition. GbRPR 2017. Lecture Notes in Computer Science(), vol 10310. Springer, Cham. https://doi.org/10.1007/978-3-319-58961-9_17
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DOI: https://doi.org/10.1007/978-3-319-58961-9_17
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