DocumentCode
3023930
Title
Web Classification Mining Based on Radial Basic Probabilistic Neural Network
Author
Gao, Meijuan ; Tian, Jingwen
Author_Institution
Dept. of Autom. Control, Beijing Union Univ., Beijing, China
fYear
2009
fDate
25-26 April 2009
Firstpage
586
Lastpage
589
Abstract
With the development and widely used of Internet and information technology, the Web has become one of the most important means to obtain information for people. According to the Web document classification and the theory of artificial neural network, a web classification mining method based on radial basic probabilistic neural network is presented in this paper. We construct the structure of radial basic probabilistic neural network that used for Web text information classification, and adopt the k-nearest neighbor algorithm and least square method to train the network. The structure of web classification mining system based on radial basic probabilistic neural network is given. With the ability of strong pattern classification and function approach and fast convergence of radial basic probabilistic neural network, the classification mining method can truly classify the Web text information. The actual classification results show that this method is feasible and effective.
Keywords
Internet; data mining; learning (artificial intelligence); least squares approximations; neural nets; pattern classification; Internet; Web classification mining; Web document classification; Web text information classification; artificial neural network; information technology; k-nearest neighbor algorithm; least square method; radial basic probabilistic neural network; Artificial neural networks; Convergence; Data mining; Databases; IP networks; Information technology; Least squares methods; Neural networks; Neurons; Pattern classification; Web mining; classification; probabilistic neural network; radial basic function;
fLanguage
English
Publisher
ieee
Conference_Titel
Database Technology and Applications, 2009 First International Workshop on
Conference_Location
Wuhan, Hubei
Print_ISBN
978-0-7695-3604-0
Type
conf
DOI
10.1109/DBTA.2009.88
Filename
5207691
Link To Document