The presentation to lecture of Athena I. Vakali (Aristotle University of Thessaloniki, Greece) on May 13th, 2004 is possible to obtain at the page:
The need to employ various clustering methodologies and
practices on the Web increases, due to the exponential growth
of data and users interactions over the Internet. Earlier research
efforts have focused both on understanding the Web user needs
and on organizing web data sources (e.g. pages, documents).
- Clustering methodologies have been proven beneficial in terms of
grouping Web users in clusters such that the various information
circulation activities can be facilitated. More specifically, similarity
measures have been proposed towards capturing Web users common
practices whereas effective Web user logs processing has resulted in the
definition of user session patterns. Knowledge about such
commonalities/differences between users facilitates procedures employed
in various fields such as e-commerce, dissemination of information etc.
- Various approaches for Clustering of Web sources have been proposed
since the number of web sources has been increasing rapidly.
In this context, the idea of compound documents and logical information
units has been quite evolving recently. Moreover, the notion
of Web page communities has gain ground lately in order to organize web
sources and meet web user requirements.
This talk will overview the most popular methodologies and
implementations in terms of clustering either Web users or Web sources
and will present a survey about current status and future trends in
clustering employed over the Web.
Athena I. Vakali received a B.Sc. degree in Mathematics from the Aristotle University of Thessaloniki, Greece, a M.Sc. degree in Computer Science from Purdue University, USA (with a Fulbright scholarship) and a Ph.D. degree in Computer Science from the Department of Informatics at the Aristotle University of Thessaloniki. Since 1997, she is a faculty member of the Department of Informatics, Aristotle University of Thessaloniki, Greece. Her research interests include Web data management, focusing on the performance and analysis of Web (as well as XML) data storage, caching and clustering.
Presentation:11.06.2004