Abstract
Density-based clustering, such as Density Peak Clustering (DPC) and DBSCAN, can find clusters with arbitrary shapes and have wide applications such as image processing, spatial data mining and text mining. In DBSCAN, a core point has density greater than a threshold, and can spread its cluster ID to its neighbours. However, the core points selected by one cut/threshold are too coarse to segment fine clusters that are sensitive to densities. DPC resolves this problem by finding a data point with the peak density as centre to develop a fine cluster. Unfortunately, a DPC cluster that comprises only one centre may be too fine to form a natural cluster. In this paper, we provide a novel clustering of multiple density peaks (MDPC) to find clusters with arbitrary number of regional centres with local peak densities through extending DPC. In MDPC, we generate fine seed clusters containing single density peaks, and form clusters with multiple density peaks by merging those clusters that are close to each other and have similar density distributions. Comprehensive experiments have been conducted on both synthetic and real-world datasets to demonstrate the accuracy and effectiveness of MDPC compared with DPC, DBSCAN and other base-line clustering algorithms.
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This work was partially supported by Australia Research Council (ARC) DECRA Project (DE140100387).
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Cai, B. et al. (2018). Clustering of Multiple Density Peaks. In: Phung, D., Tseng, V., Webb, G., Ho, B., Ganji, M., Rashidi, L. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2018. Lecture Notes in Computer Science(), vol 10939. Springer, Cham. https://doi.org/10.1007/978-3-319-93040-4_33
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DOI: https://doi.org/10.1007/978-3-319-93040-4_33
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