Information Retrieval from Search Engine Using Particle Swarm Optimization

Abstract

The World Wide Web provides an ample amount of information to the users, however, this leads to difficulty in the identification of relevant content. Web mining could be considered as a cure to this problem. It includes the application of machine learning and data mining techniques, which helps in the automatic extraction of meaningful patterns and relationships from a huge cluster of web data. Web mining is categorized into three areas: (i) web content mining, digging out knowledge from the content (i.e., text and graphics) of web pages, (ii) web structure mining, which extracts information from data describing the organization of web content, and (iii) web usage mining, in which we gather patterns by looking at the interactions of the users with the web. There is no sharply defined variation among these categories, and all the three mining tasks can be combined. So our paper focus on the very different type of technique for searching the web content, where we perform mining of the web content efficiently and give the fruitful results for the users. We planned to apply the support vector machine technique and the Particle Swarm Optimization (PSO) algorithm for searching the web content and giving the efficient and best results.