Performance Analysis Of Similarity Coefficients In Web Information Retrieval Using Genetic Algorithm
Vikas Thada, Vivek Jaglan · 2014
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 topic[1].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-Urbani/Buser 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-Urbani/Buser similarity coefficients.