Association Analysis of Student Database Using Data Mining for Making Better Admission Policy in Colleges | Original Article
Educational data mining is one of prominent field for higher educational institutes and colleges, as they are applying techniques of data mining to analyses their big data about their students. These analysis as a results, have been used for making better policies regarding academic quality, placements and admissions for their institutes or colleges. In this study, author has developed a prediction model by analysis of vast database of enrolled student of commerce department of career college Bhopal using data mining technique called association rule mining. Online mining tool called XLMiner has been used for this study. Author has selected some of main parameters or information about student like Gender, Caste, Subject, District, Semester and parent’s occupation from the dataset of student for this analysis. By mining these data, author has found many interesting hidden patterns and relationship (affinity) among these parameters as set of rules. These rules can be used to predict the viewpoints or opinions of student and their guardian in perspectives to admission and on the basis of these patterns, admission cell of colleges can make efficient policies to increase the rate of admission in particular course during upcoming academic sessions.