The Internet has become a popular medium to disseminate information and a new platform to conduct electronic business (e-business) and electronic commerce (e-commerce). With the rapid growth of the WWW and the intensified competition among the businesses, effective web presence is critical to attract potential customers and retain current customer thus the success of the business. This poses a significant challenge because the web is inherently dynamic and web data is more sophisticated, diverse, and dynamic than traditional well-structured data. Web mining is one method to gain insights into how to evolve the web presence and to ultimately produce a predictive model such that the evolution of a given web site can be categorized under its particular context for strategic planning. In particular, web logs contain potentially useful information and the analysis of web log data have opened new avenues to assist the web administrators and designers to establish adaptive web presence and evolution to fit user requirements.
Broadly speaking, web log analysis falls into the range of web usage mining, one of the three categories of web mining (Kosala and Blockeel, 2000; Srivastava et al., 2002). There are several steps involved in web log analysis: web log acquisition, cleansing and preprocessing, and pattern discovery and analysis.