Data Mining : An Intelligent tool

What is Data Mining
Datamining is the process of discovering knowledge out of pile of huge data. Corporates and scientists have been applying datamining techniques since long. Datamining helps to uncover those knowledge or problems which has been occuring in the system in correlation with other events and we hardly felt and realize it. Data mining in iSchool brings out meaningful new correlations, behaviour patterns, and performance trends of students by analyzing large amounts of data stored in repositories, by using Knowledge Discovery tools,as well as statistical and mathematical techniques.

Data mining uses a combination of an explicit knowledge base, sophisticated analytical skills, and domain knowledge to uncover hidden trends and patterns.
Why Data mining in Higher Education

CEON believes that one of the biggest challenges that higher education faces today is predicting the paths of students and alumni.

Education Industry today is confronting a unique set of challenges. Owing to competition, most institutions are facing reduced budgets, yet at the same time they have an increased need for services and efficiency enhancement. Technology should strengthen your Institute to deliver a superior teaching and learning environment where students grow and thrive. iSchool is a complete suite of applications that enables capture, manipulation and presentation of data in a meaningful manner, anytime and anywhere.

One way to effectively address these students and alumni challenges is through the analysis and presentation of data or data mining. Data mining can be used to predict individual behavior with high accuracy. As a result of this insight, institutions are able to allocate resources and staff more effectively. Data mining may, for example, give an institution the information necessary to take action before a student drops out, or to efficiently allocate resources with an accurate estimate of how many students will take a particular course.

Data mining is a powerful tool for academic intervention. Through data mining, a university could, for example, predict with 85 percent accuracy that,which students will or will not graduate. The university could use this information to concentrate academic assistance on those students most at risk.