Web page classification based on k-nearest neighbor approach

Authors:
Oh-Woog Kwon;Jong-Hyeok Lee
Affiliations:
Dept. of Computer Science and Engineering, Pohang University of Science and Technology, San 31 Hyoja Dong, Pohang, 790-784, Korea;Dept. of Computer Science and Engineering, Pohang University of Science and Technology, San 31 Hyoja Dong, Pohang, 790-784, Korea
Venue:
IRAL '00 Proceedings of the fifth international workshop on on Information retrieval with Asian languages
Year:
2000

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Abstract

Automatic categorization is the only viable method to deal with the scaling problem of the World Wide Web. In this paper, we propose a Web page classifier based on an adaptation of k-Nearest Neighbor (k-NN) approach. To improve the performance of k-NN approach, we supplement k-NN approach with a feature selection method and a term-weighting scheme using markup tags, and reform document-document similarity measure used in vector space model. In our experiments on a Korean commercial Web directory, our proposed methods in k-NN approach for Web page classification improved the performance of classification.