International Journal on Recent and Innovation Trends in Computing and Communication ISSN: 2321-8169 Volume: 2 Issue: 9 2918 – 2922 _______________________________________________________________________________________________ 2918 IJRITCC | September 2014, Available @ http://www.ijritcc.org _______________________________________________________________________________________ Extracting Interests of Users from Web Log Data Log B Swetha M. Tech student, Dept of CSE KSRMCE KADAPA, INDIA swetha.sahana@gmail.com K Srinivasa Rao Associate Professor, Dept of CSE KSRMCE KADAPA, INDIA srinu532@gmail.com Abstract —The knowledge on the cobweb is growing expressively. Without a recommendation theory, the clients may come through lots of instance on the network in finding the knowledge they are stimulated in. Today, many web recommendation theories cannot give clients adequate symbolized help but provide the client with lots of immaterial knowledge. One of the main reasons is that it can't accurately extract user's interests. Therefore, analyzing users' Web Log Data and extracting users' potential interested domains become very important and challenging research topics of web usage mining. If users' interests can be automatically detected from users' Web Log Data, they can be used for information recommendation and marketing which are useful for both users and Web site developers. In this paper, some novel algorithms are proposed to mine users' interests. The algorithms are based on visit time and visit density which can be obtained from an analysis of web users' Web Log Data. The experimental results of the proposed methods succeed in finding user's interested domains. Keywords- Web Mining, Web Usage Mining, Data Mining, Weblog data, Web Content Mining. __________________________________________________*****_________________________________________________ I. INTRODUCTION Web mining - is the application of data mining techniques to discover patterns from the Web. According to analysis targets, web mining can be divided into three different types, which are Web usage mining, Web content mining and Web structure mining. Web usage mining is the process of extracting useful information from server logs i.e. user’s history. Web usage mining is the process of finding out what users are looking for on internet. Some users might be looking at only textual data, whereas some others might be interested in multimedia data. This technology is basically concentrated upon the use of the web technologies which could help for betterment. Web structure mining is the process of using graph theory to analyze the node and connection structure of a web site. According to the type of web structural data, web structure mining can be divided a into two kinds: 1. Extracting patterns from hyperlinks in the web: a hyperlink is a structural component that connects the web page to a different location. 2. Mining the document structure: analysis of the tree-like structure of page structures to describe HTML or XML tag usage. Web content mining is the mining, extraction and integration of useful data, information and knowledge from Web page contents. The heterogeneity and the lack of structure that permeates much of the ever expanding information sources on the World Wide Web, such as hypertext documents, makes automated discovery, organization, and search and indexing tools of the Internet and the World Wide Web. The design of our group analysis and publishing search logs with privacy related web mining. Search engine companies collect the database of intentions, the histories of their user’s search queries. These search logs are a gold mine for researchers. Fig Showing Web Mining Architecture Search engines play a crucial role in the navigation through the vastness of the Web. Today’s search engines do not just collect and index web pages, they also collect and mine information about their users. They store the queries, clicks, IP-addresses, and other information about the interactions with users in what is called a search log .Search logs contain valuable information that search engines use to tailor their services better to their user’s needs. They enable the discovery of trends, patterns, and anomalies in the search behavior of users, and they can be used in the development and testing of new algorithms to improve search performance and quality. Scientists all around the world would like to tap this gold mine for their own research search engine companies, however, do not release them because they contain sensitive information about their users,