Earticle

초록

영어

In recent years, the amount of data into a geometric growth puts forward higher requirements on data mining algorithm. In the process of frequent itemsets of traditional Apriori algorithm produced, frequent itemsets' generation and storage are quite a waste of time and space. In this paper, we put forward a new Hash table and use the technology to improve the algorithm and get SamplingHT algorithm, through a lot of contrast experiments showed that the new algorithm enhances performance when frequent itemset is generated, and effectively reduce the database scan times, In order to achieve more optima.

목차

Abstract
 1. Introduction
 2. Association Rule Data Mining Technology
  2.1 Basic concept of Data Mining
  2.2 Association Rule Mining Algorithm
 3. SamplingHT Algorithm
  3.1 The Main Steps of SamplingHT Algorithm
  3.2 New Hash Function
  3.3 SamplingHT Code:
 4. Experiments and Analysis
  4.1 Experiment 1
  4.2 Experiment 2
  4.3 Experiment 3
 5. Conclusion
 Acknowledgements
 References

키워드

저자

• Zhi Liu [ Dalian Maritime University ]
• Tianhong Sun [ Dalian Maritime University ]
• Guoming Sang [ Dalian Maritime University ]

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