Beschreibung
At the present time, the amount of data stored in educational database is increasing rapidly. These databases contain hidden information for improvement of students performance. Decision tree is the most useful classification algorithm in educational data mining because of its ease of execution and easier to understand compared to other algorithms. We can get more accurate and valuable results with the help of decision tree algorithm which can be useful for instructors to improve the student learning outcomes. The ID3, C4.5 and CART decision tree algorithms has been applied on the data of students to predict their performance. But all these 3 algorithms are used only for small database. For large database, we are using a new algorithm i.e. SPRINT which removes all the memory restriction and accuracy problem comes in other algorithms. It is fast and scalable than others because it can be implemented in both serial and parallel fashion for good data placement and load balancing. In this work, SPRINT decision tree algorithm is used to solve the problem of classification in education system.
Autorenporträt
Assistant Professor in the Department of Computer Science at DSMNRU. He has been associated with the IT Industry, research and academics for the last twelve years. He has rich and diverse experience in academia and has been Visiting Professor at various Universities and Colleges.
Herstellerkennzeichnung:
OmniScriptum SRL
Str. Armeneasca 28/1, office 1
2012 Chisinau
MD
E-Mail: info@omniscriptum.com
