In this section, we discuss data mining techniques that are mostly used in web usage mining such as statistical
analysis techniques, clustering, classification, association rule mining, and sequential pattern mining.
Statistical analysis is the process of applying statistical techniques
on web log file to describe sessions, and user
navigation such as viewing the time and length of a navigational path. Statistical prediction can also be used to
predict when some page or document would be accessed from now. It makes use of the N-grammer model which
assumes that when a user is browsing a given page, the last N pages browsed affect the probability of the next page
to be visited.
Clustering is the process of partitioning a given population of events or items into sets of similar elements. In web
usage mining there are two main interesting clusters to be discovered: usage clusters, and pages clusters. An
approach is to cluster web pages to have a high quality clusters of web pages and use that clusters to produce index
pages, where index pages are web pages that have direct links to pages that may be of
interest of some group of
website navigators.
Classification is dividing an existing set of events or transactions into
another predefined sets or classes based on
some characteristics. In web usage mining, classification is used to group users
into predefined groups with respect
to their
navigation patterns in order to develop profiles of users belonging to a particular class or category.
Association rule mining is the discovery of attribute values that occur frequently together in a given set of data.
Association rules mining techniques are used in web usage mining to find pages t
hat are often viewed together, or
to show which pages tend to be visited within the same user session. A re-ranking met
hod with the help of website
taxonomy is to mine for generalized association rules and abstract access patter
ns of different levels to improve the
performance of site search. Another approach for predicting web log accesses is based on association rule mining.
Association rule mining facilitates the identification of related pages or navigation patterns which can be used in
web personalization.
In sequential pattern mining a sequence of actions or events is determined with respect to time or other sequences.
In web usage mining, sequential pattern
mining could be used to predict user’s future visit behaviors. Some web
usage
mining and analysis tools use sequential pattern mining to extract interesting patterns such as Speed Tracer and
Web miner.
In this section, we discuss data mining techniques that are mostly used in web usage mining such as statistical
analysis techniques, clustering, classification, association rule mining, and sequential pattern mining.
Statistical analysis is the process of applying statistical techniques
on web log file to describe sessions, and user
navigation such as viewing the time and length of a navigational path. Statistical prediction can also be used to
predict when some page or document would be accessed from now. It makes use of the N-grammer model which
assumes that when a user is browsing a given page, the last N pages browsed affect the probability of the next page
to be visited.
Clustering is the process of partitioning a given population of events or items into sets of similar elements. In web
usage mining there are two main interesting clusters to be discovered: usage clusters, and pages clusters. An
approach is to cluster web pages to have a high quality clusters of web pages and use that clusters to produce index
pages, where index pages are web pages that have direct links to pages that may be of
interest of some group of
website navigators.
Classification is dividing an existing set of events or transactions into
another predefined sets or classes based on
some characteristics. In web usage mining, classification is used to group users
into predefined groups with respect
to their
navigation patterns in order to develop profiles of users belonging to a particular class or category.
Association rule mining is the discovery of attribute values that occur frequently together in a given set of data.
Association rules mining techniques are used in web usage mining to find pages t
hat are often viewed together, or
to show which pages tend to be visited within the same user session. A re-ranking met
hod with the help of website
taxonomy is to mine for generalized association rules and abstract access patter
ns of different levels to improve the
performance of site search. Another approach for predicting web log accesses is based on association rule mining.
Association rule mining facilitates the identification of related pages or navigation patterns which can be used in
web personalization.
In sequential pattern mining a sequence of actions or events is determined with respect to time or other sequences.
In web usage mining, sequential pattern
mining could be used to predict user’s future visit behaviors. Some web
usage
mining and analysis tools use sequential pattern mining to extract interesting patterns such as Speed Tracer and
Web miner.
การแปล กรุณารอสักครู่..