Abstract
Focused web crawlers collect topic-related web pages from the Internet. Using Q learning and semi-supervised learning theories, this study proposes an online semi-supervised clustering approach for topical web crawlers (SCTWC) to select the most topic-related URL to crawl based on the scores of the URLs in the unvisited list. The scores are calculated based on the fuzzy class memberships and the Q values of the unlabelled URLs. Experimental results show that SCTWC increases the crawling performance.
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