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Open AccessArticle

Density Peak Clustering Algorithm Considering Topological Features

1
School of Information Science and Engineering, Shandong Normal University, Jinan 250358, China
2
School of Light Industry Science and Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250351, China
3
Department of Electrical Engineering and Information Technology, Shandong University of Science and Technology, Jinan 250031, China
*
Authors to whom correspondence should be addressed.
Electronics 2020, 9(3), 459; https://doi.org/10.3390/electronics9030459
Received: 18 January 2020 / Revised: 5 March 2020 / Accepted: 6 March 2020 / Published: 8 March 2020
(This article belongs to the Special Issue Data Analysis in Intelligent Communication Systems)
The clustering algorithm plays an important role in data mining and image processing. The breakthrough of algorithm precision and method directly affects the direction and progress of the following research. At present, types of clustering algorithms are mainly divided into hierarchical, density-based, grid-based and model-based ones. This paper mainly studies the Clustering by Fast Search and Find of Density Peaks (CFSFDP) algorithm, which is a new clustering method based on density. The algorithm has the characteristics of no iterative process, few parameters and high precision. However, we found that the clustering algorithm did not consider the original topological characteristics of the data. We also found that the clustering data is similar to the social network nodes mentioned in DeepWalk, which satisfied power-law distribution. In this study, we tried to consider the topological characteristics of the graph in the clustering algorithm. Based on previous studies, we propose a clustering algorithm that adds the topological characteristics of original data on the basis of the CFSFDP algorithm. Our experimental results show that the clustering algorithm with topological features significantly improves the clustering effect and proves that the addition of topological features is effective and feasible. View Full-Text
Keywords: clustering; graph; topological features clustering; graph; topological features
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Lu, S.; Zheng, Y.; Luo, R.; Jia, W.; Lian, J.; Li, C. Density Peak Clustering Algorithm Considering Topological Features. Electronics 2020, 9, 459.

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