Earticle

초록

영어

Web crawling is an important approach for collecting larger-scale web data on, and keeping up with, the rapidly expanding Internet. This paper puts forward the improved shark search approach for crawling large-scale Web data based on link clustering and the technology of tunnel. In this study we focus on the classification of Web links instead of downloaded web pages to determine relevancy which can avoid local optimum of the traditional shark search algorithm. The experiments show that the improve shark search algorithm can provide the simplest alternative for conquering the issue of instantaneous page which are ranked lowly allied to the given topic at hand.

목차

Abstract
 1. Introduction
 2. The Shark-Search Algorithm and Related Work
 3. The Improvement of the Shark-Search
  3.1. Adding Link Clustering to the algorithm
  3.2. Adding Tunneling
 4. Experimental Results
 5. Conclusions and Future Work
 Acknowledgment
 References

키워드

저자

• Youwei Yuan [ School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang, P.R China, 310018 ]
• Dou Chen [ School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang, P.R China, 310018 ]
• Yong Li [ School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang, P.R China, 310018 ]
• Dongjin Yu [ School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang, P.R China, 310018 ]
• Lamei Yan [ School of Media and Design, Hangzhou Dianzi University, Hangzhou, Zhejiang, P.R China, 310018 ]
• Zhixiang Zhu [ School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang, P.R China, 310018 ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605