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Data Mining

            Data Mining can help to find trends about the way users access the CTI website. Consider the visitor access patterns in the Programs and Courses sections. Limiting our attention to users who accessed the Programs section, we can perform classification analysis on users interested in Ph.D. programs versus Masters programs (including all Masters programs, Computer Science, Distributed Systems, etc). First we would separate those who were clicked a link into only Masters programs, and then separate those who clicked a link into only Ph.D. programs, and then separate out those who clicked links into both. We would also put into each of these groups, a list of links that the users clicked in the Courses section. Affinity grouping analysis using a powerful algorithm will show any patterns or general association rules between users who access the Ph.D. in the Program page and a specific Algorithm class. Data Discrimination analysis can allow us to compare users who click on the Ph.D. link in the Program page with those who clicked on the Master's links. We might find the group who click only on the Ph.D. link much more frequently and click on a link to the Algorithm class in the Courses section. We could use this knowledge to provide some cross-links within the Ph.D. program page showing users some courses like the Algorithm class that would be of interest. .
             Examine the visitor's access pattern in the courses and the programs section. If we apply affinity grouping or in other words market basket analysis we may find very interesting pattern that students in telecommunication usually prefer to take non-programming courses. This may turn into cross selling idea, if we offer more and more non-programming course in telecom program, it may attract more student in this program who don't like to do programming. We can perform classification analysis on the users who is interested in Telecommunication program versus courses offer into Telecommunication program.

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