In accordance with regulations and requirements, the editorial department's website domain has been changed to arocmag.cn. The original domain (arocmag.com) will be discontinued after Dec. 31st, 2024.
See More
Algorithm Research & Explore
|
App. Res. of Comp. Printed Article
Vol. 36, 2019 No. 8
2256-2260

Entropy-based spectral clustering algorithm for mixed type data

Jiang Zhihan1,2,3
Zhu Jun1,3
Zhou Xiaofeng1,3
Li Shuai1,2,3
1. Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2. University of Chinese Academy of Sciences, Beijing 100049, China
3. Key Laboratory of Network Control System, Chinese Academy of Sciences, Shenyang 110016, China
DOI: 10.19734/j.issn.1001-3695.2018.02.0080

Abstract

The problem that the traditional clustering algorithm can only deal with single attribute data and cannot handle the clustering problem of mixed type data very well. Most of the clustering algorithms for mixed type data currently have the problem of initializing sensitive and cannot handle the data of arbitrary shape. This paper proposed an entropy-based spectral clustering algorithm for mixed type data to deal with mixed type data. First, it proposed a new similarity measure, it used the numerical data in the spectral clustering algorithm to constitute a Gaussian kernel function of the matrix, and used the classification data to constitute an entropy-based the influence factor of the matrix. A new similarity matrix combined these two matrices. Instead of the traditional similarity matrix, it proposed the new similarity matrix avoid feature transformation and parameter adjustment between the numerical data and the classification data. Then, it applied the new similarity matrix to the spectral clustering algorithm so as to deal with the data of arbitrary shape, and finally got the clustering result. Experiments on UCI data sets show that this algorithm can effectively deal with the clustering problem of mixed attribute data, with high stability and good robustness.

Foundation Support

工信部智能制造综合标准化与新模式应用项目(Y6L8283A01)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2018.02.0080
Publish at: Application Research of Computers Printed Article, Vol. 36, 2019 No. 8
Section: Algorithm Research & Explore
Pages: 2256-2260
Serial Number: 1001-3695(2019)08-003-2256-05

Publish History

[2019-08-05] Printed Article

Cite This Article

姜智涵, 朱军, 周晓锋, 等. 基于信息熵的混合属性数据谱聚类算法 [J]. 计算机应用研究, 2019, 36 (8): 2256-2260. (Jiang Zhihan, Zhu Jun, Zhou Xiaofeng, et al. Entropy-based spectral clustering algorithm for mixed type data [J]. Application Research of Computers, 2019, 36 (8): 2256-2260. )

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.

Indexed & Evaluation

  • The Second National Periodical Award 100 Key Journals
  • Double Effect Journal of China Journal Formation
  • the Core Journal of China (Peking University 2023 Edition)
  • the Core Journal for Science
  • Chinese Science Citation Database (CSCD) Source Journals
  • RCCSE Chinese Core Academic Journals
  • Journal of China Computer Federation
  • 2020-2022 The World Journal Clout Index (WJCI) Report of Scientific and Technological Periodicals
  • Full-text Source Journal of China Science and Technology Periodicals Database
  • Source Journal of China Academic Journals Comprehensive Evaluation Database
  • Source Journals of China Academic Journals (CD-ROM Version), China Journal Network
  • 2017-2019 China Outstanding Academic Journals with International Influence (Natural Science and Engineering Technology)
  • Source Journal of Top Academic Papers (F5000) Program of China's Excellent Science and Technology Journals
  • Source Journal of China Engineering Technology Electronic Information Network and Electronic Technology Literature Database
  • Source Journal of British Science Digest (INSPEC)
  • Japan Science and Technology Agency (JST) Source Journal
  • Russian Journal of Abstracts (AJ, VINITI) Source Journals
  • Full-text Journal of EBSCO, USA
  • Cambridge Scientific Abstracts (Natural Sciences) (CSA(NS)) core journals
  • Poland Copernicus Index (IC)
  • Ulrichsweb (USA)