Web page classification based on Semi-supervised Naïve Bayesian EM algorithm

Author

Zhixing, Wang ; Shaohong, Chen

Author_Institution

Comput. Center, East China Normal Univ., Shanghai, China

fYear

2011

fDate

27-29 May 2011

Firstpage

242

Lastpage

245

Abstract

Web Classification is one of the hot researches in Web mining field. Within the exploded Internet information circumstance, most pages are unlabeled. This paper has proposed a Naïve Bayesian EM algorithm classification method based on the feature of Semi-supervised machine learning. The method used Hierarchical Clustering EM framework to train Naïve Bayesian Classifier iteratively. The result of the experiment proved that the method introduced in the paper shows good effect of Web classification.

Keywords

Bayes methods; Internet; data mining; expectation-maximisation algorithm; learning (artificial intelligence); pattern classification; pattern clustering; text analysis; Internet information; Web mining; Web page classification; hierarchical clustering; semisupervised machine learning; semisupervised naive Bayesian EM algorithm; text classification; Approximation algorithms; Classification algorithms; Clustering algorithms; Hierarchical Clustering; Semi-supervised; Web page classification; web mining;

fLanguage

English

Publisher

ieee

Conference_Titel

Communication Software and Networks (ICCSN), 2011 IEEE 3rd International Conference on

Conference_Location

Xi´an

Print_ISBN

978-1-61284-485-5

Type

conf

DOI

10.1109/ICCSN.2011.6014261

Filename

6014261