Multimedia Tools and Applications

, Volume 78, Issue 23, pp 33261–33277 | Cite as

Sparse learning based on clustering by fast search and find of density peaks

  • Pengqing Li
  • Xuelian DengEmail author
  • Leyuan Zhang
  • Jiangzhang Gan
  • Jiaye Li
  • Yonggang Li


Clustering by fast search and find of density peaks (CFSFDP) is a novel clustering algorithm proposed in recent years. The algorithm has the advantages of low computational complexity and high accuracy. However, the truncation distance dc needs to be determined according to user experience. Aiming to overcome these drawbacks, this paper proposes a new algorithm named Sparse learning based on clustering by fast search and find of density peaks (SL-CFSFDP). Compared to CFSFDP, the proposed algorithm can obtain dc automatically, and it uses sparse learning to determine the neighbors of each data point, removing irrelevant data points at the same time. SL-CFSFDP combines the local density and the distance δi to automatically determine cluster centers, after which the remaining data points are assigned to clusters according to the local density and distance δi. Extensive experimental results on both synthetic and benchmark datasets show that SL-CFSFDP is superior to DBSCAN and CFSFDP.


Truncation distance Sparse learning Local density Density peaks Clustering algorithm 



This work is partially supported by the China Key Research Program (Grant No: 2016YFB1000905); the Key Program of the National Natural Science Foundation of China (Grant No: 61836016); the Natural Science Foundation of China (Grants No: 61876046, 61573270, 81701780 and 61672177); the Project of Guangxi Science and Technology (GuiKeAD17195062); the Guangxi Natural Science Foundation (Grant No: 2015GXNSFCB139011, 2017GXNSFBA198221); the Guangxi Collaborative Innovation Center of Multi-Source Information Integration and Intelligent Processing; the Guangxi High Institutions Program of Introducing 100 High-Level Overseas Talents; the Project of Guangxi Science and Technology (GuiKeAD17195062) the Guangxi Collaborative Innovation Center of Multi-Source Information Integration and Intelligent Processing; the Research Fund of Guangxi Key Lab of Multisource Information Mining & Security (18-A-01-01); the National Natural Science Foundation of Guangxi (No. 2016GXNSFAA380098) and Research Fund of Guangxi Key Lab of Multi-source Information Mining & Security (MIMS18-09).


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Pengqing Li
    • 1
  • Xuelian Deng
    • 1
    • 2
    Email author
  • Leyuan Zhang
    • 1
  • Jiangzhang Gan
    • 1
  • Jiaye Li
    • 1
  • Yonggang Li
    • 1
  1. 1.Guangxi Key Lab of Multi-source Information Mining and SecurityGuangxi Normal UniversityGuilinPeople’s Republic of China
  2. 2.College of Public Health and ManagementGuangxi University of Chinese MedicineNanningChina

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