Abstract:
Web mining generates web access patterns,
web structures, regularity and dynamics of web
contents. Association rules are used to describe
what items are frequently bought together. One
could also use them in web usage mining to
describe the pages that are often visited together.
This paper presents time-constraint association
rules by extensions of Apriori algorithm, where
Support and confidence ratios are also computed.
Current web usage mining algorithms based on
association rule do not consider the time sequence
of web usage data. Time-constraint association
used in this paper not only maintains the
sequential information, but also set the time frame
of the web usage data. In time constraint
association rule, time ratios express the
conditional probability of X and Y occurring in the
time window defined by t1 and t2, given the fact
that X and Y are accessed together in the same
session. This system generates association rules
based on time constraints; i.e; in which, access
pattern occur in the same time frame. Rules
generated from this system can be applied to
recommender system, where there is more
relationship between pages with time constraint.
