Abstract
The aim of this study is to improve web log mining and online navigation pattern prediction. Web mining is an active and wide area which incorporates several usages for the web site design, providing personalization server and other business making decisions etc. Efficient web log mining results and online navigational pattern prediction is a tough process due to vast development in web. It includes the process such as data cleaning, session identification and clustering of web logs generally. In this study initially the web log data is preprocessed and sessions are identified using refined time-out based heuristic for session identification. Then for pattern discovery a density based clustering algorithm is used. Finally for online navigation pattern prediction a new technique of SVM classification is used, which rectifies time complexity with increased prediction accuracy.
Highlights
World Wide Web is a huge repository of web pages and links
Web data mining is the application of data mining techniques in web data
Web Usage Mining applies mining techniques in log data to extract the behavior of users which is used in various applications like personalized services, adaptive web sites, customer profiling, prefetching, creating attractive web sites etc
Summary
World Wide Web is a huge repository of web pages and links It provides abundance of information for the Internet users. Web data mining is the application of data mining techniques in web data It automatically discovers and extracts information from Web documents and services (Etzioni, 1996). Web Usage Mining applies mining techniques in log data to extract the behavior of users which is used in various applications like personalized services, adaptive web sites, customer profiling, prefetching, creating attractive web sites etc. It consists of main three categories, Web usage mining, Web structure mining and Web content mining. Web content mining aims to extract/mine useful information or knowledge from Web page contents
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