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Density decay graph-based density peak clustering
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DOI：10.1016/j.knosys.2021.107075.png)
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In 2014, Rodriguez and Laio proposed a famous clustering algorithm based on a fast search and find density peaks dubbed as DPC (Rodriguez and Laio, 2014). DPC has been widely used in many fields because of its simplicity and effectiveness. However, DPC has two obvious drawbacks. First, initial cluster centers must be manually selected from the decision graph, which is extremely difficult and even impossible in some cases. Second, the result of DPC is affected by the so called chain reaction'' problem. To overcome these two drawbacks, we propose a density decay graph-based density peak clustering (DGDPC) algorithm. The key to DGDPC is the concept of density decay graph, which is inspired by the decay phenomenon (a common phenomenon in nature). In our algorithm, we form initial clusters according to density decay graphs and then merge clusters based on a simple method. As a result, there is no need to manually select cluster centers and the chain reaction problem is alleviated. Although DGDPC introduces an additional parameter m, m is robust and easy to determine in advance. Experiments on 10 synthetic datasets and 10 real-world datasets show that our algorithm outperforms DPC, DGB (Wu and Wilamowski, 2016), K-means (MacQueen et al., 1967), DBSCAN (Ester et al., 1996), and Single-link (Murtagh and Contreras, 2012) in most cases. (C) 2021 Elsevier B.V. All rights reserved.
Keyword:
Clustering algorithm
Density peaks
Density Decay Graph
Decay effect
期刊
K
IF:
7.6
论文数:
1.3W
被引数:
4.5W
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