Abstract
World Wide Web is a huge repository of web pages and links. It provides profusion of information for the Internet users. The growth of web is tremendous as approximately one million pages are added daily. User’s accesses are recorded in web logs. Because of the incredible usage of web, the web log files are growing at a faster rate and the size is becoming huge. Web data mining is the application of data mining techniques in web data. Web log Mining applies mining techniques in log data to extract the behaviour of users. Web log mining consists of three phases pre-processing, pattern discovery and pattern analysis. Web log data is usually noisy and ambiguous and pre-processing is an important process before web log mining. For discovering patterns sessions are to be constructed efficiently. This paper presents the existing work done to extracting patterns by using decision tree methodology in the technique of web log mining.
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