Incremental learning in students classification system with efficient knowledge transformation

The amount of students data in the educational databases is growing day by day, so the knowledge taken out from these data need to be updated continuously. In the circumstances, where there is a need of handling continuous flow of student´s data, there is a challenge of how to handle this massive amount of data into the information and how to accommodate new knowledge introduces with the new data. In this paper, the adaptive incremental learning algorithm for Students classification system is proposed, which competently transforms the knowledge throughout the system and also detects the new concept class efficiently. In this paper, conceptual view of the system is designed with the algorithm and experimental results on the student´s data as well as some available data sets are used to prove the efficiency of the proposed algorithm.