Mining Traversal Patterns on the WWW
Using a Meaningful Candidate Approach
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Since the World Wide Web appeared, more and more useful information has been available on the WWW. In order to find the hidden information, one application of data mining on the WWW, referred to as Web mining, has become a research area with increasing importance. Mining traversal patterns is one of the important topics in Web mining. It focuses on how to find the Web page sequences which are frequently browsed by users. Mining such information could help webmasters improve the structure of Web sites or make better marketing strategies in an e-commerce environment. Although traditional algorit...
Since the World Wide Web appeared, more and more
useful information has been available on the WWW. In
order to find the hidden information, one
application of data mining on the WWW, referred to
as Web mining, has become a research area with
increasing importance. Mining traversal patterns is
one of the important topics in Web mining. It
focuses on how to find the Web page sequences which
are frequently browsed by users. Mining such
information could help webmasters improve the
structure of Web sites or make better marketing
strategies in an e-commerce environment. Although
traditional algorithms for mining association rules
could be applied to mine traversal patterns, they
may generate too many invalid candidate patterns and
do not work efficiently. This book, therefore,
provides three new algorithms for mining traversal
patterns. These algorithms make use of the property
of Web transactions and generate meaningful
candidate patterns. Moreover, they apply several
techniques to decrease the times of database scan.
Therefore, they could provide better performance
than traditional algorithms in terms of processing
time.
useful information has been available on the WWW. In
order to find the hidden information, one
application of data mining on the WWW, referred to
as Web mining, has become a research area with
increasing importance. Mining traversal patterns is
one of the important topics in Web mining. It
focuses on how to find the Web page sequences which
are frequently browsed by users. Mining such
information could help webmasters improve the
structure of Web sites or make better marketing
strategies in an e-commerce environment. Although
traditional algorithms for mining association rules
could be applied to mine traversal patterns, they
may generate too many invalid candidate patterns and
do not work efficiently. This book, therefore,
provides three new algorithms for mining traversal
patterns. These algorithms make use of the property
of Web transactions and generate meaningful
candidate patterns. Moreover, they apply several
techniques to decrease the times of database scan.
Therefore, they could provide better performance
than traditional algorithms in terms of processing
time.
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