Comparative analysis of TOFEL IBT Result rate Among Students using K-Means Clustering
J. Emmanual Robin1, G. Prabu2
1J. Emmanual Robin, Asst Professor, Department of Computer Applications, Jayaram College of Engg &Tech, Tiruchirappalli Tamil Nadu, India.
2G. Prabu, Associate Professor, Department of Science and Engineering, Jayaram College of Engg &Tech, Tiruchirappalli, Tamil Nadu, India.
Manuscript received on November 17, 2011. | Revised Manuscript received on November 29, 2011. | Manuscript published on January 05, 2012. | PP: 15-19 | Volume-1 Issue-6, January 2012. | Retrieval Number: F0255101511/2012©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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) Percentile Mark UG (b)Percentile Mark PG (c)Percentile Mark Other
Keywords: K-Means Clustering, IBT(Internet Based Test), TOEFL(Test of English as a Foreign Language).