This document discusses how data mining techniques can be applied in higher education to analyze educational data and improve various aspects of the student experience and institutional effectiveness. It provides an overview of common data mining methods like classification, clustering, association rule mining and their uses in higher education for applications such as student performance analysis, course recommendation systems, dropout prediction, and curriculum improvement. It also addresses potential issues around privacy, data security and ethics when using data mining in education.