Automatic mining of cognitive metadata using fuzzy inference
Proceedings of the 22nd ACM conference on Hypertext and hypermedia
Automatic metadata mining from multilingual enterprise content
Web Semantics: Science, Services and Agents on the World Wide Web
Fuzzy nearest neighbor algorithms: Taxonomy, experimental analysis and prospects
Information Sciences: an International Journal
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Web document classification is an important technique of web mining. Web pages classification has been studied extensively since the Internet has become a huge database of information. The k-NN is a simple classification algorithm that is used to assign patterns of unknown classification to the class of the majority of its k nearest neighbors of known classification according to the distance measure, but a main drawback of the method is that each of the patterns of known classification is considered equally important in the assignment of the pattern to be classified. Fuzzy k-nearest neighbor (fuzzy k-NN) is improving algorithm of k-NN, which is applied successfully in structural data classification. This paper presents the web document classification based on fuzzy k-NN network, in the process of classification, TF/IDF (term frequency / inverse document frequency) is adopted for selecting features of document, to increase the accuracy and suit for real world, membership grade is used. Experimental results show that classification performance is better than both k-NN and support vector machine (SVM).