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Academic Journal of Computing & Information Science, 2021, 4(2); doi: 10.25236/AJCIS.2021.040201.

An improved density peaks clustering algorithm based on CURE


Baiyan Chen, Kai Zhou

Corresponding Author:
Baiyan Chen

School of Computer & Software, Nanjing University of Information Science & Technology, Jiangsu Nanjing, China


As a new density-based clustering algorithm, clustering by fast search and find of Density Peaks (DP) algorithm regards each density peak as a potential clustering center when dealing with a single cluster with multiple density peaks, therefore it is difficult to determine the correct number of clusters in the data set. To solve this problem, a mixed density peak clustering algorithm namely C-DP was proposed. Firstly, the density peak points were considered as the initial clustering centers and the dataset was divided into sub-clusters. Then, learned from the Clustering Using Representatives algorithm (CURE), the scattered representative points were selected from the sub-clusters, the clusters of the representative point pairs with the smallest distance were merged, and a parameter contraction factor was introduced to control the shape of the clusters. The experimental results show that the C-DP algorithm has better clustering effect than the DP algorithm. The comparison of the F-measure Index shows that the C-DP algorithm improves the accuracy of clustering when datasets contain multiple density peaks in a single cluster.


Density Peak, Hierarchical Clustering, Cluster Merging, Representative Point, Contraction Factor

Cite This Paper

Baiyan Chen, Kai Zhou. An improved density peaks clustering algorithm based on CURE. Academic Journal of Computing & Information Science (2021), Vol. 4, Issue 2: 1-6. https://doi.org/10.25236/AJCIS.2021.040201.


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