Efficiently Mining of Effective Web Traversal Patterns with Average Utility

Abstract

Mining of frequent web traversal patterns finds pages that co-occur in an order in a transaction. Patterns only with frequency would not provide sufficient information about the preferences or interestingness of users. By adopting utility-based mining model in web traversal pattern mining, users “interestingness of patterns could be found by considering time spent on the web pages as utility or user preferences. However, an issue identified in the existing utility-based web traversal pattern mining algorithms is that transaction weighted utility of a pattern is computed considering utilities of all transactions in which it exists and thus includes utilities of all patterns occurring before the pattern, irrespective of its prefixes in those transactions. This leads to generation of more candidate patterns unnecessarily and affects the efficiency of the algorithm. Another issue is that, if the length of a pattern increases, automatically its utility will also increase. With the effect of pattern length, really good utility patterns cannot be identified among all high utility patterns. Also, longer patterns with less page utility in a transaction may result in higher values and it will be treated same as shorter patterns with more page utility. By considering high average utility patterns rather patterns with only actual utility, we could distinguish high utility patterns with respect to their lengths. Moreover, a high average utility pattern would be more effective than a high utility pattern and we could reveal better results by finding effective traversal patterns in a database. With this motivation, an efficient algorithm addressing the above said issues has been proposed to discover effective web traversal patterns in a transaction database. Experiments results on real datasets show that the proposed algorithm is more efficient than an existing approach.