Earticle

초록

영어

With manufacturing technology developing persistently, hardware manufacturing cost becomes lower and lower. More and more computers equipped with multiple CPUs and enormous data disk emerge. Existing programming modes make people unable to make effective use of growing computational resources. Hence cloud computing appears. With the utilization of Map Reduce parallelized model, existing computing and storage capabilities are effectively integrated and powerful distributed computing ability is provided. Firstly, transform Apriori algorithm to Map Reduce model; realize Apriori parallel transformation; then use the way of compressing original transaction sets to improve the performance of Apriori algorithm in Hadoop framework; lastly, Map Reduce-Apriori algorithm is realized which is highly scalable for running in cloud computing environment.

목차

Abstract
 1. Introduction
 2. Algorithm Analysis and Parallelization Transformation
 3. Data Initialization
 4. Iterative implementation
  4.1 Calculate Frequent Itemsets at the kth layer
  4.2. Calculate Candidate Itemsets at the (K+1) Th Layer
 5. Generation of Association Rules
 6. Experimental Analysis and Results
  6.1. Experimental Data Set
  6.2. Experimental Test Analysis
  6.3. Analysis of Data Mining Results
 7. Conclusion
 References

키워드

저자

• Haili Xu [ Jiamusi College, Heilongjiang University of Chinese Medicine, China ]
• Feng Qi [ Jiamusi College, Heilongjiang University of Chinese Medicine, China ] Corresponding Author

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