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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 36, 2019 No. 11
3224-3229

Multi-objective differential evolution automatic clustering algorithm based on class-center density

Shen Xiaoninga,b
Sun Yia,b
Xue Yunyonga,b
Sun Shuaia,b
a. CICAEET, b. B-DAT, School of Automation, Nanjing University of Information Science & Technology, Nanjing 210044, China
DOI: 10.19734/j.issn.1001-3695.2018.04.0288

Abstract

In the process of clustering, for the reason that the randomness of the class-center selection may lead to the phenomenon that the selected class-center deviates from the data set, or the class-center is too centralized, the proposed algorithm selected the class-center for two times: it screened out the class-centers which have too small density or have small distances between pairs of class-centers, and the algorithm did not allow them to participate in clustering. Then the algorithm continued to cluster the remaining class-centers. In order to make the algorithm get the optimal class-center quickly, it proposed an improved clustering criterion function to penalize the number of clusters dynamically. In order to evaluate the performance of the proposed algorithm on clustering problems, it carried out experiments on two types of data sets. Compared with the other three existing automatic clustering algorithms, simulation experiments show that the proposed algorithm can obtain better clustering results, which validates the effectiveness of the proposed strategies.

Foundation Support

国家自然科学基金资助项目(61502239)
江苏省自然科学基金资助项目(BK20150924)
“江苏省青蓝工程”资助项目

Publish Information

DOI: 10.19734/j.issn.1001-3695.2018.04.0288
Publish at: Application Research of Computers Printed Article, Vol. 36, 2019 No. 11
Section: Algorithm Research & Explore
Pages: 3224-3229
Serial Number: 1001-3695(2019)11-004-3224-06

Publish History

[2019-11-05] Printed Article

Cite This Article

申晓宁, 孙毅, 薛云勇, 等. 采用类心密度策略的多目标微分自动聚类算法 [J]. 计算机应用研究, 2019, 36 (11): 3224-3229. (Shen Xiaoning, Sun Yi, Xue Yunyong, et al. Multi-objective differential evolution automatic clustering algorithm based on class-center density [J]. Application Research of Computers, 2019, 36 (11): 3224-3229. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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