CLUSTERING STUDENT PERFORMANCE DATA USING k-MEANS ALGORITHMS

Education institutions store large amounts of data regarding students, such as demographics, academic-related data, and student activities. These were recorded stored in many ways, including different filing systems database formats. By having these education have a better way to manage understand their students. In addition, information related students can easily be accessed extracted. As more is stored, this could allow the educational institution make informed decisions give educators good insight into system. The research approach known mining (EDM) focuses on using techniques extract massive from context transform it knowledge that improve decisions. Clustering, an unsupervised learning technique, one most powerful machine- tools for discovering patterns unseen data. This work aims provide insights obtained Oman Portal (OEP) student’s performance by manipulating k-means algorithm.