M. Roja, N. Vijay Gopal


We provide several algorithms to solve this innovative mining problem through three stages: processed to extract probabilistic issues and identify sessions for multiple users, generate all STP candidates with support values ​​(expected) for each user growth patterns, and decide on URSTP by searching for a rare user analysis Sensitive in derived STPs. Little information is inevitable, extensive survey is available. Easily support the idea of ​​the most popular scale to evaluate sequential pattern pattern, defined as the quantity or sequence ratio containing the pattern information in the target database. Patterns acquired are not always interesting for this purpose to be reduced rare but meaningful patterns representing custom and abnormal individual behaviors due to low support. We advised a framework for solving this issue in a practical way and designing algorithms to assist in the interview. In the beginning, we offer pre-treatment procedures with the extraction of heuristic methods and the identification of sessions. This identity method can be considered a sequence between the items purchased and selected by STP and the probabilistic issues that occur within the purchased documents related to a particular cycle. The results indicate that our approach can certainly capture personal behaviors of online users and express them in an understandable way.


Web Mining; Sequential Patterns; Document Streams; Dynamic Programming;


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