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Efficient Clustering Algorithms in Educational Data Mining

Efficient Clustering Algorithms in Educational Data Mining

Anupama Chadha
Copyright: © 2018 |Pages: 34
ISBN13: 9781522537250|ISBN10: 1522537252|EISBN13: 9781522537267
DOI: 10.4018/978-1-5225-3725-0.ch015
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MLA

Chadha, Anupama. "Efficient Clustering Algorithms in Educational Data Mining." Handbook of Research on Knowledge Management for Contemporary Business Environments, edited by Armando Malheiro, et al., IGI Global, 2018, pp. 279-312. https://doi.org/10.4018/978-1-5225-3725-0.ch015

APA

Chadha, A. (2018). Efficient Clustering Algorithms in Educational Data Mining. In A. Malheiro, F. Ribeiro, G. Leal Jamil, J. Rascao, & O. Mealha (Eds.), Handbook of Research on Knowledge Management for Contemporary Business Environments (pp. 279-312). IGI Global. https://doi.org/10.4018/978-1-5225-3725-0.ch015

Chicago

Chadha, Anupama. "Efficient Clustering Algorithms in Educational Data Mining." In Handbook of Research on Knowledge Management for Contemporary Business Environments, edited by Armando Malheiro, et al., 279-312. Hershey, PA: IGI Global, 2018. https://doi.org/10.4018/978-1-5225-3725-0.ch015

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Abstract

Higher education institutions are competing for excellence, and in this process, they are utilizing information technologies to gather relevant information for achieving academic excellence. The institutes are putting greater emphasis on meeting students' academic needs, enhancing the quality of service provided to students, providing better placements, course excellence, etc. The use of modern information technologies helps in storing huge data but requires the use of data mining technologies to extract useful information and knowledge from this data. Some of the knowledge achievable for higher education institutes through implementing several data mining techniques (classification, association learning, clustering, etc.) is the correlation between specialization and the chosen employment path, determining the subjects, courses, labs with high degree of difficulty, interesting subjects, courses, labs, facilities that might attract new students, etc. This chapter explores efficient clustering algorithms in educational data mining.

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