Web Usage Mining Using Artificial Ant Colony
Clustering and Genetic Programming
Proceedings of the 2003 Congress on Evolutionary
Computation CEC2003, page 1384--1391. Canberra, IEEE Press, (8-12 December 2003)Abstract
The rapid e-commerce growth has made both business
community and customers face a new situation. Due to
intense competition on one hand and the customer's
option to choose from several alternatives business
community has realized the necessity of intelligent
marketing strategies and relationship management. Web
usage mining attempts to discover useful knowledge from
the secondary data obtained from the interactions of
the users with the Web. Web usage mining has become
very critical for effective Web site management,
creating adaptive Web sites, business and support
services, personalization, network traffic flow
analysis and so on. The study of ant colonies behavior
and their self-organizing capabilities is of interest
to knowledge retrieval/management and decision support
systems sciences, because it provides models of
distributed adaptive organization, which are useful to
solve difficult optimization, classification, and
distributed control problems, among others. In this
paper, we propose an ant clustering algorithm to
discover Web usage patterns (data clusters) and a
linear genetic programming approach to analyze the
visitor trends. Empirical results clearly shows that
ant colony clustering performs well when compared to a
self-organizing map (for clustering Web usage patterns)
even though the performance accuracy is not that
efficient when comparared to evolutionary-fuzzy
clustering (i-miner) approach.
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