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Comparative analysis of TOFEL IBT result rate among students using K-Means Clustering

Author(s): J.Emmanual Robin | G.Prabu

Journal: International Journal of Soft Computing & Engineering
ISSN 2231-2307

Volume: 1;
Issue: 6;
Start page: 15;
Date: 2012;
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Keywords: K-Means Clustering | IBT(Internet Based Test) | TOEFL(Test of English as a Foreign Language)

Data mining technology that blends traditional data analysis methods with sophisticated algorithms for processing large volumes of data. This paper reveals the comparative analysis of the students with UG,PG,Other Students community. Before getting into the picture we have to know the basic concept of clustering technique. What is clustering analysis? Clustering analysis divides data into the groups (clusters) that are meaningful or useful or both. If meaningful groups are the goal, then the clusters should capture natural structure of data. This paper focuses to discover the comparative analysis of reading, writing, speaking, listening skills over the student’s dataset such as(a)PercentileMarkUG(b)PercentileMarkPG (c)PercentileMarkOther
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