Web Usage Mining Techniques

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
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 . The work in
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
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 . The authors
in  present an approach 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.

In clustering techniques are applied to web log file to discover those subsets of web pages that need to be connected, and to improve the already connected pages. The work in  uses the Competitive
Agglomeration Clustering Algorithm to cluster the sessions extracted from web log
server into typical session profiles of users.

Classification
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
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 that are often viewed together, or to show which
pages tend to be visited within the same user session . The work introduced
in proposes a re-ranking method with the help of website taxonomy to mine
for generalized association rules and abstract access patterns of different levels to improve the performance of site search.
Sequential Pattern Mining :

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 SpeedTracer and Webminer . The authors in  suggest
using adaptive websites to attract customers using sequential patterns to display
special offers dynamically to them.