Abstract
Density-based clustering algorithms have become a popular research topic in recent years. However, most these algorithms have difficulties identifying all clusters with greatly varying densities and arbitrary shapes or have considerable time complexity. To tackle this issue, we propose a novel density assessment method by using mutual nearest neighbor, and then propose a relative density clustering algorithm (RDC). RDC can get the right number of clusters for the datasets, which include varying densities and arbitrary shapes; in addition, the time complexity of it is O(nlog n).
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Acknowledgment
Thanks for the guidance and suggestions of my tutor. This work is funded by the Science and Technology Development Fund Macau (SKL-IOTSC-2021-2023) and Graduate Scientific Research and Innovation Foundation of Chongqing, China (no. CYS20067).
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Zhang, Y., Zha, Y., Li, L., Xiong, Z. (2021). A New Density Clustering Method Using Mutual Nearest Neighbor. In: U, L.H., Spaniol, M., Sakurai, Y., Chen, J. (eds) Web and Big Data. APWeb-WAIM 2021. Lecture Notes in Computer Science(), vol 12858. Springer, Cham. https://doi.org/10.1007/978-3-030-85896-4_38
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DOI: https://doi.org/10.1007/978-3-030-85896-4_38
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