Mining Analysis of SIS Database Using Rough Set Theory
P. Ramasubramanian, Kombiah Iyakutti, Vijaykumar Sureshkumar, P. Thangavelu · 2007
One of the biggest challenges that higher education faces today is predicting the paths of students. Institutions would like to know, something about the performance of the students like which group of students will complete the course successfully and which group of students need more assistance. Behrouz-Minaei-Bidgoli in 2004[3], investigated a web based educational system using data mining techniques. He proposed to develop a tool to find out the effects of the different types of students' problems and their performances. These problems will be classified into different patterns based on the level of students like normal, average and below average. The present paper attempts to analyse SIS database using rough set theory to predict the future of students. In fact two main cases of missing attribute values are considered here "lost" (the original value was erased) and "do not care" (the original value was irrelevant). Keywords: Data Mining, Knowledge discovery, Rough set, Rough Set Analysis, Prediction, Lower approximation and Upper approximation.