Improving Effectiveness Of Web Sites Using
Incremental Data Mining Over Clickstreams
Author(s)
F. Cavalcanti & O. Belo
Abstract
The increasing necessity of organizational data exploration and analysis, seeking
new knowledge that may be implicit in operational systems, has made the study
of data mining techniques gain a huge impulse. This impulse can be clearly
noticed in the e-commerce domain, where the analysis of a client’s past
behaviours is extremely valuable and may, eventually, bring up important
working instruments for determining their future behaviour. Typical databases
are continuously changing, which can invalidate some patterns or introduce new
ones. Thus, conventional data mining techniques were proved to be inefficient,
as they needed to re-execute to update the mining results with the ones derived
from the database changes. To solve this problem and ensure a better
performance in data mining processes, incremental mining techniques have
emerged. In this paper, we analyze some existing incremental mining strategies
and models, giving particular emphasis to their application on Web sites, in order
to develop models to discover Web users behaviour patterns and automatically
generate some recommendations to restructure sites in useful time.
Keywords: data mining, clickstreams, algorithms and strategies for incremental
data mining and Web site restructuring.
1 Introduction
Daily, in extremely dynamic market sectors, companies challenge a situation
where information quality and knowledge over them play an important and vital
role. In order to publish their goods and products to the customers, so they can be
largely consumed, companies need to understand how customers behave in the
market. The customer behaviour analysis can provide valuable information for a
Keywords
data mining, clickstreams, algorithms and strategies for incremental
data mining and Web site restructuring.
Related Book
Other papers in this volume
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