Student Behavior Mining on Intelligent Desktop and Finding their Interests

Mansour Esmaeilpour, Zarina Shukur, and Vahideh Naderifar


Student behavior mining , social network, user friendly, sequence pattern mining


Students have special goals when using social networks (such as computer) and when dealing with system segments and sections which can be different from person to person. If students tend to have the system works in a user friendly forms, this system must be able to present suggestion of response in order to simplify their affairs with this very system. In this paper we assumed as being students and study the student's behavior mining when using system, tools and social networks. At first our proposed system surveys the behavior of students as a whole and records these surveys in a dataset. Then our system categorizes, and finds what the students’ interests are all about, and outlines these interests to announce them as a series of recommendations in order to increase the use of user friendly of the system. By studying students' behavior mining, the system can be able to find necessary and useful tools, software as well as ways of accessing to social networks. The system categorizes these tools, provide listings and so on. This suggestion not only helps avoiding confusion in tools mining again, but helps avoiding wasting significant time as well. This method could be performed by data mining in which a complete graph has been used in this study. Finally we compared this method with other similar methodological application and our methodological approach is more superior in terms of run time than the other methods.

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