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|Title:||McDPC : multi-center density peak clustering||Authors:||Wang, Yizhang
|Keywords:||Engineering::Computer science and engineering||Issue Date:||2020||Source:||Wang, Y., Wang, D., Zhang, X., Pang, W., Miao, C., Tan, A.-H., & Zhou, Y. (2020). McDPC : multi-center density peak clustering. Neural Computing and Applications, 32(17), 13465-13478. doi:10.1007/s00521-020-04754-5||Journal:||Neural Computing and Applications||Abstract:||Density peak clustering (DPC) is a recently developed density-based clustering algorithm that achieves competitive performance in a non-iterative manner. DPC is capable of effectively handling clusters with single density peak (single center), i.e., based on DPC’s hypothesis, one and only one data point is chosen as the center of any cluster. However, DPC may fail to identify clusters with multiple density peaks (multi-centers) and may not be able to identify natural clusters whose centers have relatively lower local density. To address these limitations, we propose a novel clustering algorithm based on a hierarchical approach, named multi-center density peak clustering (McDPC). Firstly, based on a widely adopted hypothesis that the potential cluster centers are relatively far away from each other. McDPC obtains centers of the initial micro-clusters (named representative data points) whose minimum distance to the other higher-density data points are relatively larger. Secondly, the representative data points are autonomously categorized into different density levels. Finally, McDPC deals with micro-clusters at each level and if necessary, merges the micro-clusters at a specific level into one cluster to identify multi-center clusters. To evaluate the effectiveness of our proposed McDPC algorithm, we conduct experiments on both synthetic and real-world datasets and benchmark the performance of McDPC against other state-of-the-art clustering algorithms. We also apply McDPC to perform image segmentation and facial recognition to further demonstrate its capability in dealing with real-world applications. The experimental results show that our method achieves promising performance.||URI:||https://hdl.handle.net/10356/144300||ISSN:||0941-0643||DOI:||10.1007/s00521-020-04754-5||Rights:||© 2020 Springer-Verlag London Limited. This is a post-peer-review, pre-copyedit version of an article published in Neural Computing and Applications. The final authenticated version is available online at: http://dx.doi.org/10.1007/s00521-020-04754-5||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||SCSE Journal Articles|
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