Predicting Scholarship Grants Using Data Mining Techniques
Allemar Jhone P. Delima · International Journal of Machine Learning and Computing · 2019
Every university has its own methods to realize scholarship assessment.Scholarships are established by schools as a motivation for students that have outstanding and exceptional achievements.It is a form of incentive and benefit to inspire and embolden students and improve schools' learning principles [1].From that point of view, an optimal way is considered to assess and distribute scholarship grants.Data Mining is the process of extracting information from large data sets through the use of algorithms and methods drawn from the field of statistics and Database Management Systems [2].Clustering, decision trees, genetic algorithms, Bayes classifiers, association rules, neural networks, and support vector machines to name some, are the algorithms and methods that can be used in data mining analysis that allow getting important information from the database [3].One of the most popular data mining techniques is clustering.It represents an unsupervised learning method whose objective is to divide the data set so that the distance among the clusters should be minimal, whereas the inter-cluster distance should be maximal.One of the methods frequently used in data partition is the k-means data.The k-means algorithm is the easiest and the most common algorithm based on the squared error criterion [3].The study clustered the indexed data of the different scholarship programs offered in Surigao State College of