Earticle

초록

영어

Massive calculation tasks always show as a regular problem in the area of data mining. Many traditional data mining algorithms can only deal with small-scale input data and will run slower or even collapse when the input data increase. The problem above is always a bottleneck of traditional data mining algorithm. Better performance can be achieved if we can transplant these algorithms in cloud computing platform and make them run in parallel. Thus, whether the algorithm can be run in parallel properly or not becomes the key to solve the problem mentioned above. By analyzing the process of local linear regression algorithm, the bottleneck and the aspect which can be parallelized in these algorithms corresponding MapReduced algorithms are proposed, which handle the key problem of efficiency successfully. The research achievements gained in this paper provide a solution for MapReducing algorithms of data mining, and the experiment results show the effectiveness of the solution.

목차

Abstract
 1. Introduction
 2. Local Weighted Linear Regression Algorithm
 3. Steps Included in the Locally Weighted Linear Regression Algorithm
  3.1. Determine Neighboring Data Points
  3.2 Local Data Point Weighted Processing
  3.3. Determination of Linear Regression Function and Regression Coefficient
  3.4. Prediction Calculation
 4. Implementation of Local Weighted Linear Regression Algorithm in MapReduce
  4.1. Partition of Datanode
  4.2. Map Stage
  4.3. Reduce Stage
 5. Experimental Analysis and Results
 6. Conclusion
 References

키워드

저자

• Chuanying Lu [ JiLin Communications Polytechnic, Changchun, 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