Clustering of Web Log Data to Analyze User Navigation Patterns

As we know the amount of data available online is increasing day by day, the World Wide Web has becoming one of the most valuable resources for information retrievals and knowledge discoveries. Web mining technologies are the right solutions for knowledge discovery on the Web. Application of web usage data can be used to better understand web usage, and apply this specific knowledge to better serve users. Web usage mining is the base for navigation pattern mining and approach of clustering is used to perform that Mining, usage mining deals with the discovery and analysis of usage patterns from Web data, specifically web logs, in order to improve web based applications. A web navigation behavior is helpful in understanding what information of online users demand. In our study we extract the common pattern and do clustering, following that, the analyzed results can be seen as knowledge to be used in intelligent online applications, refining web site maps, web based personalization system and improving searching accuracy when seeking. The experimental results shows the clusters of navigation patterns of user and also the approach can improve the quality of clustering for user navigation pattern in web usage mining systems. These results can be used for predicting user's intuition in the large web sites.