Abstract
Web Data mining can be defined as knowledge discovery and analysis of useful information from the web. It is the process of determining user accessibility pattern, during the mining of log files and associated data from a particular Web site. Accurate web log mining results and efficient online navigational pattern prediction are undeniably crucial for tuning up websites and consequently helping in visitor’s retention. Like any other data mining task, web log mining starts with data cleaning and preparation and it ends up discovering some hidden knowledge which cannot be extracted using conventional methods. We are proposing an enhancement to the web log mining process and to the online navigational pattern prediction named Dynamic Apriori algorithm for mining both frequent and closed frequent item set over data stream in log file. The algorithm is appropriate for noticing latest or new changes in the set of frequent item set by modifying the traditional Apriori algorithm and Travelling Salesmen Problem (TSP) in the arriving data stream.
The proposed modified algorithm we avoid unwanted and repetition of data. Here experimental analysis also use for improvement of web design, efficiently finding the User web navigation pattern which will help to grow the customer satisfaction and analysis.