A Fuzzy Based Approach for Student Admission Recommendation into Seminary
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Abstract
Education is a complex systematic engineering, which is the guarantee of training high-quality talent, helping society make full use of educational outcomes and promote the healthy development of education. To step-in to that high quality education students have a chaos in evaluating the best among the several institutions and select the one. In this paper, we suggest an automatic student admission recommendation system that selects a set of institutions by considering a set of student prerequisites and semantically match them with their parameters. Fuzzy clustering technique is applied on categorized data for suggesting better suited colleges for a particular student based on his/her course option. Since the same student can opt for more than one college for a particular course, depending upon multiple parameters, fuzzy clustering acts as the best suited method for seminary recommendation. The relative fuzzy score called “degree of membership†calculated for each college indicates the membership of a particular student to different institutions. Subjective evaluation of the algorithm is tested on synthetic dataset and the experiments produce promising results.
Keywords: Synthetic dataset, Fuzzy sets, Clustering, Text Categorization, Smart selection
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