A web document classification approach based on fuzzy association concept

Authors:
Jingsheng Lei;Yaohong Kang;Chunyan Lu;Zhang Yan
Affiliations:
College of Information Science and Technology, Hainan University, Haikou, P.R. China;College of Information Science and Technology, Hainan University, Haikou, P.R. China;College of Information Science and Technology, Hainan University, Haikou, P.R. China;College of Information Science and Technology, Hainan University, Haikou, P.R. China
Venue:
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part I
Year:
2005

Citing 4
Cited 1

Syntactic clustering of the Web

Selected papers from the sixth international conference on World Wide Web
Hierarchical classification of Web content

SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Web Mining: Information and Pattern Discovery on the World Wide Web

ICTAI '97 Proceedings of the 9th International Conference on Tools with Artificial Intelligence
Mining longest repeating subsequences to predict world wide web surfing

USITS'99 Proceedings of the 2nd conference on USENIX Symposium on Internet Technologies and Systems - Volume 2

Intelligent Information Retrieval Using Fuzzy Association Rule Classifier

International Journal of Intelligent Information Technologies

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, a method of automatically identifying topics for Web documents via a classification technique is proposed. Web documents tend to have unpredictable characteristics, i.e. differences in length, quality and authorship. Motivated by these fuzzy characteristics, we adopt the fuzzy association concept to classify the documents into some predefined categories or topics. The experimental results show that our approach yields higher classification accuracy compared to the vector space model.