Earticle

초록

영어

Nowadays with the rapid development of wireless sensor networks, and network traffic monitoring, stream data gradually becomes one of the most popular data models. Stream data is different from the traditional static data. Clustering analysis is an important technology for data mining, so that many researchers pay their attention to the clustering of stream data. In this paper, MSFS algorithm is proposed. By means of the experimental verification analysis, based on biologically inspired computational model, higher clustering purity on both the real dataset and the simulation datasets existence is demonstrated for the proposed algorithm. In other words, the cluster result of MSFS algorithm is advantageous over previous method.

목차

Abstract
 1. Introduction
 2. Related Work
 3. MSF Model Introduction
 4. MSFS Algorithm
  4.1. Related Concepts
  4.2. The Specific Process of the Algorithm
  4.3. Performance Analysis
 5. Experimental Results
  5.1. Real Data Sets
  5.2. Synthetic Data Sets
 6. Discussion and Conclusions
 Acknowledgments
 References

키워드

저자

• Yingmei Li [ College of Computer Science and Information Engineering, Harbin Normal University, 150025 Harbin, China ]
• Min Li [ College of Computer Science and Information Engineering, Harbin Normal University, 150025 Harbin, China ]
• Jingbo Shao [ College of Computer Science and Information Engineering, Harbin Normal University, 150025 Harbin, China ]
• Gaoyang Wang [ College of Computer Science and Information Engineering, Harbin Normal University, 150025 Harbin, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605