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. 41, 2024 No. 10
2955-2961

Deep active semi-supervised clustering model

Fu Yanyana,b,c
Huang Ruizhanga,b,c
Xue Jingjinga,b,c
Ren Linaa,b,c
Chen Yanpinga,b,c
Lin Chuana,b,c
a. Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry, b. State Key Laboratory of Public Big Data, c. College of Computer Science & Technology, Guizhou University, Guiyang 550025, China
DOI: 10.19734/j.issn.1001-3695.2024.01.0025

Abstract

Deep semi-supervised clustering aims to achieve better clustering results using a small amount of supervised information. However, the amount of supervised information is often limited due to the expensive labelling cost. Therefore, with limited supervised information, it becomes crucial to select the most valuable supervisory information for clustering. To address the above problem, this paper proposed a deep active semi-supervised clustering model(DASCM) which designed an active learning method that was able to select marginal texts containing rich information and further generated high-value supervised information containing edge texts. The model used this supervised information to guide the clustering, thus improving the clustering performance. The experimental results on five real text datasets show that the clustering performance of DASCM is significantly improved. This result verifies that supervised information generated using active learning methods that cover marginal text is effective in improving clustering.

Foundation Support

国家自然科学基金资助项目(62066007)
贵州省科技支撑计划资助项目(黔科合支撑【2022】一般277)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.01.0025
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 10
Section: Algorithm Research & Explore
Pages: 2955-2961
Serial Number: 1001-3695(2024)10-011-2955-07

Publish History

[2024-04-23] Accepted Paper
[2024-10-05] Printed Article

Cite This Article

付艳艳, 黄瑞章, 薛菁菁, 等. 基于主动学习的深度半监督聚类模型 [J]. 计算机应用研究, 2024, 41 (10): 2955-2961. (Fu Yanyan, Huang Ruizhang, Xue Jingjing, et al. Deep active semi-supervised clustering model [J]. Application Research of Computers, 2024, 41 (10): 2955-2961. )

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)