원문정보
보안공학연구지원센터(IJDTA)
International Journal of Database Theory and Application
Vol.9 No.7
2016.07
pp.269-278
피인용수 : 0건 (자료제공 : 네이버학술정보)
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
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
저자정보
참고문헌
자료제공 : 네이버학술정보