Generation and analysis of educational datasets using k-means clustering

Educational Data Mining is a growing field exploring data in educational perspective by applying diverse data mining tools. It provides built-in knowledge of teaching and learning method for successful education preparation. In the history of data mining, k-means algorithm plays an important role because of its extensive implementation. Data mining in educational datasets can be applied to discover patterns/clusters in the untrusted datasets to computerize the decision making process of institutional actors responsible for improving the higher educational quality. k-Means is an algorithm to classify the objects on the basis of features of attributes in K number of groups, where K indicates a positive integer. The proposed study is a combination of data mining algorithm and the new concepts of data mining, which aims to improve the quality by combining the institutional actor’s data with the knowledge which has been extracted from databases, and providing the precise advice to the concern in scientific manner.