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Special Topics in Data Analysis and Knowledge Discovery
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393-400

KMS self-learning case knowledge matching based on IG-NRS and ICK bidirectional compression

Zhang Jianhua
He Longfei
Zhang Shuwei
Cao Ziao
Wen Dandan
School of Management, Zhengzhou University, Zhengzhou 450001, China

Abstract

Case knowledge matching can effectively alleviate the knowledge overload problem and ensure the level of knowledge application. Aiming at the redundancy problem of self-learning case knowledge matching in knowledge management systems, this paper proposed a bidirectional compression method based on IG-NRS and ICK. The method firstly designed an improved model of NRS, called IG-NRS. Accordingly, it approximated the set of case knowledge attributes to achieve the vertical compression of the neighborhood decision system. On this basis, it realized horizontal compression by introducing spectral clustering discrimination and eliminating the inconsistent case knowledge. And then it determined the knowledge matching results by locking the target case knowledge clusters and the most similar case knowledge through the similarity of knowledge views. Experimental results on several UCI datasets show that this method can effectively reduce the redundancy of self-learning case knowledge in knowledge management systems and achieve higher knowledge matching efficiency and effectiveness.

Foundation Support

国家社会科学基金资助项目(19BTQ035)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.06.0259
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 2
Section: Special Topics in Data Analysis and Knowledge Discovery
Pages: 393-400
Serial Number: 1001-3695(2024)02-011-0393-08

Publish History

[2023-08-22] Accepted Paper
[2024-02-05] Printed Article

Cite This Article

张建华, 贺龙飞, 张淑唯, 等. 基于IG-NRS与ICK双向压缩的KMS自学习案例知识匹配 [J]. 计算机应用研究, 2024, 41 (2): 393-400. (Zhang Jianhua, He Longfei, Zhang Shuwei, et al. KMS self-learning case knowledge matching based on IG-NRS and ICK bidirectional compression [J]. Application Research of Computers, 2024, 41 (2): 393-400. )

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  • 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.

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