Earticle

초록

영어

With the development of network technology, the data capacity become more abundant. How to effectively manage the data, the retrieval more quickly, accurately, improve the data classification accuracy becomes crucial. The BP neural network algorithm with its learning speed, strong ability to adapt and is widely used in network in data mining. Exist but its convergence rate is not high and big error and other shortcomings, therefore, on the basis of traditional algorithm, an improved BP neural network algorithm is put forward. Low error, through experimental analysis, the improved algorithm convergence rate is better.

목차

Abstract
 1. Introduction 
 2. Related Works
  2.1. Data Mining Technology 
  2.2. The BP Neural Network Algorithm
  2.3. Improved BP Neural Network Algorithm
 3. The Application of Improved BP Network Algorithm in Data Mining
  3.1. The Data Feature Extraction
  3.2. The Structure Characteristics of Itemsets
  3.3. The Structure of the Eigenvectors
  3.4. Data Partitioning
 4. The Experimental Results and Analysis
 5. Conclusion
 References

키워드

저자

• Yang liu [ Chongqing Nanfang Translators college of SISU ]

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