Abstract:
World Wide Web is a huge repository of
web pages and links. Users’ accesses are recorded
in web logs. Because of the tremendous usage of
web, the web log files are growing at a faster rate
and the size is becoming huge. Web data mining is
the application of data mining techniques in web
data. Web Usage Mining applies mining
techniques in log data to extract the behavior of
users which is used in various applications like
personalized services, adaptive web sites,
customer profiling, prefetching, creating attractive
web sites etc., This paper presents web usage
mining where frequent patterns of accessed
patterns are generated by using hashing
approach. The main problem in generating
association rules by Apriori algorithm is
processing time. In this system, Apriori algorithm
is improved by Hashing approach. Hashing is very
efficient for the generation of candidate large
itemsets. In addition, hashing employs effective
pruning techniques to reduce the transaction
database size. Generation of smaller candidate
sets by hashing enables to effectively trim the
transaction database at a much earlier stage of
the iterations, thereby reducing the computational
cost for later iterations significantly.
