MPhil Thesis Defence


"WEB MINING FROM CLIENT SIDE USER ACTIVITY LOG"

By

Mr. Yeuk-Kiu Shun


Abstract

Data mining techniques, mainly clustering and association rule mining, are
widely applied to the World Wide Web scenario. Various applications
including server log mining, collaborative personalization are proposed
and implemented. In majority, these web mining approaches are concentrated
on helping a specific content provider or a website in restructuring or in
tailoring its contents for each individual reader. This thesis proposes
another approach to provide assistance to end-users instead. A detailed
client side user activity log is captured every time the user browses the
web. From this activity log, the user's interests are discovered and
categorized using clustering techniques; the user's web browsing habits
are discovered and formalized using association rule mining. Such
knowledge forms personalized knowledge base. Using this knowledge base, an
intelligent agent, called WBext, is able to help the user to search the
web by extending a simple keyword query. By observing and comparing user
activities with existing knowledge about user's habits and interests, the
agent is also capable of recommending links to the user while he/she is
browsing the web. The effectiveness of the approach is evaluated based on
a series of metrics; results of various experiments are presented and
analyzed.


Date:				Saturday, 17 August 2002

Time:				11:00a.m.-1:00p.m.

Venue:				Room 2303
				Lifts 17-18

Committee Members:			Prof. Hongjun Lu (Supervisor)
				Dr. Cunsheng Ding (Chairman)
				Dr. Qiong Luo


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