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COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
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SODA '00 Proceedings of the eleventh annual ACM-SIAM symposium on Discrete algorithms
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SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
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In web classification, most researchers assume that the objects to be classified are individual web pages from one or more websites. In practice, the assumption is too restrictive since a web page itself may not carry sufficient information for it to be treated as an instance of some semantic class or concept. In this paper, we relax this assumption and allow a subgraph of web pages to represent an instance of the semantic concept. Such a subgraph of web pages is known as a web unit. To construct and classify web units, we formulate the web unit mining problem and propose an iterative web unit mining (iWUM) method. The iWUM method first finds subgraphs of web pages using knowledge about website structure and connectivity among the web pages. From these web subgraphs, web units are constructed and classified into categories in an iterative manner. Our experiments using the WebKB dataset showed that iWUM was able to construct web units and classify web units with high accuracy for the more structured parts of a website.