Performance Analysis Of Similarity Coefficients In Web Information Retrieval Using Genetic Algorithm

Abstract Crawling is a process in which web search engines collect data from the web. Focused crawling is a special type of crawling process where crawler look for information related to a predefined topic1.In this paper a method for finding out the most relevant document among a set of documents for the given set of keyword is presented. Relevance checking is done with the help of Rogers-Tanimoto MountFord and Baroni-UrbaniBuser similarity coefficients. The method uses genetic algorithm to show that the average similarity of documents to the query increases when Probability of mutation is taken as low and Probability of crossover is taken as high. The method does the performance analysis of different similarity coefficients on the same set of documents and selects the best combination of ProC and ProM to achieve maximum relevancy using of Rogers-Tanimoto MountFord and Baroni-UrbaniBuser similarity coefficients.