The web is a rich data mining source which is dynamic and fast growing, providing great opportunities which are often not exploited. Web data represent a real challenge to traditional data mining techniques due to its huge amount and the unstructured nature. Web logs contain information about the interactions between visitors and the website. Analyzing these logs provides insights into visitors' behavior, usage patterns, and trends. Web usage mining, also known as web log mining, is the process of applying data mining techniques to discover useful information hidden in web server's logs. Web logs are primarily used by Web administrators to know how much traffic they get and to detect broken links and other types of errors. Web usage mining extracts useful information that can be beneficial to a number of application areas such as: web personalization, website restructuring, system performance improvement, and business intelligence. The Web usage mining process involves three main phases: pre-processing, pattern discovery, and pattern analysis. Various preprocessing techniques have been proposed to extract information from log files and group primitive data items into meaningful, lighter level abstractions that are suitable for mining, usually in forms of visitors' sessions. Major data mining techniques in web usage mining pattern discovery are: clustering, association analysis, classification, and sequential patterns discovery. This chapter discusses the process of web usage mining, its procedure, methods, and patterns discovery techniques. The chapter also presents a practical example using real web log data.
Performance Comparison of Pattern Discovery Methods on Web Log Data
