Efficient Clustering Algorithms in Educational Data Mining

Efficient Clustering Algorithms in Educational Data Mining

Anupama Chadha (Manav Rachna International Institute of Research and Studies, India)
DOI: 10.4018/978-1-5225-3725-0.ch015


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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Clustering is a technique of segregating the objects into partitions such that the objects in a group are more similar to each other than the objects in the other group. Clustering has its applications in variety of domains like health sector (Kaur, Harleen & Wasan, Krishan, Siri, 2006; Sharma, A. & Mansotra, V., 2014), E Commerce (Cheng, Yu & Ying, Xiong, 2009; Li, Mei & Feng, Cheng, 2010; Li, Yong-hong & Liu, Xiao-liang, 2010) etc. One of the areas where clustering is gaining boom is education (Hung, Jui-Long & Gao, Qingcheng, 2011; Hung, Jui-Long, Hsu, Yu-Chang & Rice, Kerry, 2012; Jin, Hanjun, Wu, Tianzhen, Liu, Zhiliang & Yan, Jianlin, 2009; Jing-miao, Zhang & Wei-xiao, Gao, 2008; Ma, Yiming, Liu, Bing, Wong, Kian, Ching, Yu, S., Philip & Lee, Ming, Shuik, 2000; Mei-lan, Chen, 2010; Ogor, 2007; Pal, 2012)

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