DEVELOPING A MODEL FOR ADMISSION CELL OF COLLEGES BY ANALYZING STUDENT DATABASE USING CLUSTERING

Today, many higher educational institutes and colleges are concentrating on student admission management process as they are facing challenges of admission and student placement so they are analyzing their existing student's historical admission dataset using various data mining techniques. These analyses are then used to take necessary action to increase the admissions in particular courses of colleges. In this research, author has presented a classification model by analyzing student admission database of 1008 students from Commerce department of Career College Bhopal during session 2014-15 to 2016-17. The main objective of this research paper is to extract the classification patterns and relations between common admission system attributes of student like Gender, Caste, Father's Occupation, Subject-branch, District etc. for enhancing the admission policy for upcoming sessions which help to College Admission Cell. The model uses simple K –Means Clustering methods in WEKA environment. This classification model presents analysis and interpretation of clusters (objects with similar groups) generated from experimental process using WEKA tool with respect to admission office/cell perspective. In this way the resultant model uncover and understand hidden patterns from the vast student database .As a result of this; colleges are able to make admission policy more effectively.