Utility Pattern Approach for Mining High Utility Log Items from Web Log Data

. Mining frequent log items is an active area in data mining that aims at searching interesting relationships between items in databases. It can be used to address a wide variety of problems such as discovering association rules, sequential patterns, correlations and much more. Weblog that analyzes a Web site's access log and reports the number of visitors, views, hits, most frequently visited pages, and so forth. Mining frequent log items from web log data can help to optimize the structure of a web site and improve the performance of web servers. Existing methods often generate a huge set of potential high utility log items and their mining performance is degraded consequently. Two novel algorithms as well as a compact data structure for efficiently discovering high utility log items are proposed. High utility log items are maintained in a tree-based data structure called utility pattern tree. Implementing mining process is done through Discarding Local Unpromising Items and Decreasing Local Node Utility strategies. Experimental results predict that these strategies can keep track of previously accessed pages of a user, identify needed links to improve the overall performance of a web page, and improve the actual design of web pages with only two database scans.