FREQUENT PATTERN MINING ON ONLINE JUDGE EDUCATION WEB LOG DATA USING ECLAT ALGORITHM

Abstract

The World Wide Web is a popular and interactive tool for spreading information today that is expanding rapidly. Web mining is the process of extracting patterns and implicit data from artifacts or activities associated to the World Wide Web in order to find relevant and potentially helpful patterns. This extracted data can also be utilized to enhance web personalization, fraud detection, future prediction accessed by the user, user profiling, and understanding of user web activity. Upcoming page prediction is one example of how this information can be used to improve web usage mining. The proposed system consists of three phases which are data pre-processing, pattern discovery, and pattern analysis. Raw web log data may contain noise and impurities. By using some data preprocessing techniques that noise will be removed. Data preprocessing phase is the most important one because it makes the data with good quality. In Pattern discovery phase, the users’ navigational pattern and rules are extracted by using association rule algorithm. In this thesis, first preprocessing can be done with data cleaning, user identification, page identification and session identification. The objective of this thesis is to identify the frequent pattern from Online Judge web log data of web server of the University by using the ECLAT algorithm. We modified a very efficient ECLAT algorithm for matching interesting new patterns and even used support and confidence to calculate interesting pattern measures.