It is indispensable that the users surfing on the Internet could have web pages classified into a given topic as correct as possible. As a result, topic-driven crawlers are becoming important tools to support applications such as specialized web portals, online searching, and competitive intelligence. This paper presents a topic-driven crawler computing the degree of relevance and refining the preliminary set of related web pages using term frequency/document frequency, entropy, and compiled rules. This paper also gives a kind of comparatively ideal system architecture and the relationship of each module of a topic-driven crawler, and describes several modules on the details.
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