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A novel approach with focused crawling for various anchor texts is discussed in this paper. Most of the search engines search the web with the anchor text to retrieve the relevant pages and answer the queries given by the users. The crawler usually searches the web pages and filters the unnecessary pages which can be done through focused crawling. A focused crawler generates its boundary to crawl the relevant pages based on the link and ignores the irrelevant pages on the web. In this paper, an effective focused crawling method is implemented to improve the quality of the search. Here, three learning phases are considered namely, content-based, link-based and sibling-based learning are undergone to improve the navigation of the search. In this approach, the crawler crawls through the relevant pages efficiently and more relevant pages are retrieved in an effective way. It is proved experimentally that more number of relevant pages are retrieved for different anchor texts with three learning phases using focused crawling.

Title: A novel approach with focused crawling for various anchor texts is discussed in this paper.Background: Most of the search engines search the web with the anchor text to retrieve the relevant pages and answer the queries given by the users. The crawler usually searches the web pages and filters the unnecessary pages which can be done through focused crawling. A focused crawler generates its boundary to crawl the relevant pages based on the link and ignores the irrelevant pages on the web. Methods and findings:In this paper, an effective focused crawling method is implemented to improve the quality of the search. Here, three learning phases are considered namely, content-based, link-based and sibling-based learning are undergone to improve the navigation of the search. In this approach, the crawler crawls through the relevant pages efficiently and more relevant pages are retrieved in an effective way. Conclusion:It is proved experimentally that more number of relevant pages are retrieved for different anchor texts with three learning phases using focused crawling.

Web has become an integral part of our lives and search engines play an important role in making users search the content online using specific topic. The web is a huge and highly dynamic environment which is growing exponentially in content and developing fast in structure. No search engine can cover the whole web, but it has to focus on the most valuable pages for crawling. Many methods have been developed based on link and text content analysis for retrieving the pages. Topic-specific web crawler collects the relevant web pages of interested topics of the user from the web. In this paper, we present an algorithm that covers the link, text content using Levenshtein distance and probability method to fetch more number of relevant pages based on the topic specified by the user. Evaluation illustrates that the proposed web crawler collects the best web pages under user interests during the earlier period of crawling.

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