Prediction of students’ employability using clustering algorithm: A hybrid approach

Abstract

Data Mining is a process of exploring the huge data in search of reliable patterns and methodical relationship among variables. As a result, the findings may be validated through applying the detected patterns to a novel subset of the data. In simple words, Data Mining is referred as extracting the useful information as large dataset and transforming into reliable structure for future use. Data Mining has shown its incredible performance in various fields to a greater extent, out of which, Educational Data Mining (EDM) is one among them. Many researchers have addressed huge number of problems in EDM and applied various techniques to reveal the useful and hidden information that helped in the process of decision making. Students getting employed during and after graduation are one of the important parts of their life. Students, based on their academic performances, are getting employed in companies they deserve. But still, the probability of getting employed is very less in this competitive world. In this paper, a real-time scenario has been chosen for analyzing various factors for getting employed/unemployed. Various clustering and classification techniques have been implemented and their performances are studied. A hybrid approach is presented in this paper that integrates the benefits of particle swarm optimization (PSO) and fuzzy clustering means (FCMs). The results obtained show that the proposed technique helps in obtaining higher accuracy to other clustering techniques. The proposed clustering algorithm PSO-FCM, accuracy is 34.4%, 36.45% and 28.45% higher than the existing method, time complexity shows 45%, 33% and 49% lower than the existing [Formula: see text]-means clustering, Naïve Bayes clustering and SVM clustering algorithms, respectively.