A Model for Mining Course Information using Vague Association Rule

There are different university offering different types of courses over several years, and the biggest issue with that is how to get information to make course more effective. Association rule mining can be used to evaluate the course effectiveness and helps to look for in regards to changes in performance of the course. For Example there is a course offering different topics. We can say that the topics having full attendance are totally effective and carry no hesitation information. While there are some topics which are almost fully attendant carry some hesitation information. This hesitation information is valuable and can be used to make the course more effective and interesting. We use vague association rule to render that hesitation information and develop an algorithm to mine the hesitation information. Our experiments on real datasets verify that our algorithm to mine the Vague Association Rule is efficient. Compared with the traditional Association Rule mined from transactional databases, the Vague Association Rule mined from the AH-pair databases are more specific and are able to capture richer information.