Abstract
Analysis of Web logs is one of the important challenges to provide Web intelligent services. Association rule mining algorithms are used widely to track users' web behaviour. Due to large amount of data many times the rules formed by these algorithms are very long and redundant. Recently Constraintbased mining approaches have received attention to deal with these big and redundant association rules. In this paper we discuss the Constraint based web mining approach used to reduce the size of association rules derived from Web log. The approach proves effective in reducing the overlap of information and also improves the efficiency of mining tasks. Constraint-based mining enables users to concentrate on mining their interested association rules instead of the complete set of association rules.
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