Online transaction data between online visitors and online functionalities usually convey users’ taskoriented behavior models. Grouping online transactions might be captured knowledge which provides information, in return, creating user accounts, which may be associated with different navigational models. Some future online applications like, online recommendations or online personalized applications, the previous related works is most important to make online users get their preferred information accurately. We demonstrated usability and scalability of the proposed approach through performing experiments on two real world data sets. The practical results have proved the method’s effectiveness in comparison with some previous studies.
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