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System Development & Application
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App. Res. of Comp. Printed Article
Vol. 36, 2019 No. 1
148-152

Dimension reduction for information tables based on knowledge partition of neighborhood rough set

Peng Xiaorana
Liu Zunrenb
Ji Junb
a. College of Data Science & Software Engineering, b. College of Computer Science & Technology, Qingdao University, Qingdao Shandong 266071, China
DOI: 10.19734/j.issn.1001-3695.2017.06.0650

Abstract

Knowledge reduction of Pawlak rough set includes two parts: knowledge reduction for decision tables and know-ledge reduction for information tables. As an extension of Pawlak rough set, neighborhood rough set is widely applied to attri-bute reduction for decision tables, but rarely applied to attribute reduction for information tables. In order to design an attri-bute reduction algorithm suitable for information tables, this paper first proposed a knowledge reduction criterion of neighborhood rough set for information tables based on the knowledge reduction criterion of Pawlak rough set. Then, according to this criterion, this paper proposed a new attribute reduction algorithm for information tables with greedy strategy, which was applicable to clustering. Compared with PCA algorithm, the experimental results show that by using the proposed algorithm to reduce dimensions of data sets, the number of attributes in the reduction sets is more, and the accuracy of K-means algorithm is higher according to the reduction sets. It proves that the proposed algorithm can be effectively applied to attribute reduction for information tables.

Foundation Support

国家自然科学基金资助项目(61503208)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2017.06.0650
Publish at: Application Research of Computers Printed Article, Vol. 36, 2019 No. 1
Section: System Development & Application
Pages: 148-152
Serial Number: 1001-3695(2019)01-034-0148-05

Publish History

[2019-01-05] Printed Article

Cite This Article

彭潇然, 刘遵仁, 纪俊. 基于邻域粗糙集下知识划分的信息表降维 [J]. 计算机应用研究, 2019, 36 (1): 148-152. (Peng Xiaoran, Liu Zunren, Ji Jun. Dimension reduction for information tables based on knowledge partition of neighborhood rough set [J]. Application Research of Computers, 2019, 36 (1): 148-152. )

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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