Earticle

초록

영어

Cloud computing contains a huge amount of data, which are featured as being widely distributed, heterogeneous, and dynamic. Thus, aiming at how to mine useful parts in these information, this paper proposes an Apriori algorithm based on cloud computing and introduces cost-sensitive learning and non-filter matrix to find k frequency set and uses the method of generating association rules to improve effectiveness of data mining. Simulation experiments show that mining algorithm in this paper is highly effective and suitable for data mining in the context of cloud computing.

목차

Abstract
 1. Introduction
 2. Description of Basic Algorithm
 3. Apriori Algorithm based on Cost-Sensitive Non-frequent Filter Matrix
  3.1 Cost-Sensitive Learning
  3.2 Seek k –Frequency Set by Using Non-Frequency Filter Matrix
  3.3 Generate Strong Correlation Rules
  3.4 Generate Non-Frequent Filter Matrix
 4. Data Mining in Cloud Computing
 5. Analysis of Improved Apriori Algorithm in Cloud Computing
 6. Conclusion
 References

키워드

저자

• Jiangang Jin [ Software Technology Vocational College, North China University of Water Resources and Electric Power, Zhengzhou 450045, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Grid and Distributed Computing

• 간기

  격월간

• pISSN

  2005-4262

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605