Abstract: Discovering the hidden knowledge from large volume of student database and applying it properly for decision making is essential for ensuring high quality education in any academic institution. This knowledge is extractable through data mining techniques. Association Rule mining technique aims at discovering implicative tendencies that can provide valuable information for the decision maker. In this project, we are going to present an applied research on mining Association Rule using academic data of a university and use it for syllabus design. We are going to discover knowledge regarding the academic performance and personal statistics of students. And we will develop a technique to transform the existing relational database for studentís academic performance into a universal database format using academic and personal data of a student. After that we will transform the universal format into a modified format for suitability of using Association Rule mining algorithm. We will use FP Growth algorithm for finding interested association rules from the transformed database which can be useful to extract knowledge of studentís academic progress, decay in their potentiality, abandonment as well as retention of students. The impact of courses and curriculum and teaching methodologies are also found from the extracted knowledge which is beneficial for any institution of higher education.
Keywords: Data Mining, Reason Find out, Fp-growth Algorithm.