Abstract:
Data mining methods are used to discover
the behaviour of the users. Therefore, the data
used for the mining purpose must be qualified for
the data cleaning stage and must be considered
and planned efficiently to meet the requirement.
For this reason, the data cleaning of the preprocessing
stage becomes the essential key.
Similarity measurement method is used to discover
web usage data that have same category or usage
purpose for clustering. Association rule mining
uses the clustered data to generate rules that
discover the patterns of interest.
This proposed system presents web usage mining
using data mining methods. The main components
that are included in this system are the
preprocessing of web access log, computing
similarity measurement using Jaccard coefficient,
clustering the web pages using K-Mean Algorithm
and finally the generation of rules for frequent
pattern of web pages using Apriori Algorithm for
interesting relationships among web pages in
given web usage data set.
