خلاصة:
Web usage mining is a type of web mining, which exploits data mining techniques
to discover valuable information from navigation behavior of World Wide Web users. The
first phase of web usage mining is the data pre-processing phase, which includes the
session reconstruction operation from server logs. Session reconstruction success directly
affects the quality of the frequent patterns discovered in the next phase. In reactive web
usage mining techniques, the source data is web server logs and the topology of the web
pages served by the web server domain. Other kinds of information collected during the
interactive browsing of web site by user, such as cookies or web logs containing similar
information, are not used. The next phase of web usage mining is discovering frequent user
navigation patterns. In this phase, pattern discovery methods are applied on the
reconstructed sessions obtained in the first phase in order to discover frequent user patterns.
In this paper, we propose a frequent web usage pattern discovery method that can
be applied after session reconstruction phase. In order to compare accuracy performance of
session reconstruction phase and pattern discovery phase, we have used an agent simulator,
which models behavior of web users and generates web user navigation as well as the log
data kept by the web server.
Key words: web usage mining, session reconstruction, apriori technique, agent simulator
and web topology.