An Innovative K* Clustering Algorithm on Systematic Transformation of Asynchronous Regions for Estimating Education completion performance
Journal Title: International Journal of Engineering and Science Invention - Year 2019, Vol 8, Issue 1
Abstract
In present days, the educational institutions maintain volumes of data of the students. The amount of data stored in educational databases is rapidly increasing because of the increase in awareness and application of data science in the field of higher and professional education system. we can mine the hidden knowledge in the available databases for generating various analytical reports for proper decision making. This Proposal is designed to present and justify the capabilities of data mining in data science environment. The main contribution of this proposal is the Estimating Education completion performance based on Systematic Transformation of Asynchronous Regions using K* Clustering Algorithm. The data stored in the Institution Education System (IES) from 2012 to 2016 will be used to perform an analysis of study on database for final result for this thesis. The confidentiality of data is also maintained. The final outcomes will be shown that most of the students belong to the cluster which needs motivation and remedial coaching for improving their educational capabilities. The dataset can be improved by including data of students currently enrolled during 2017-18 also. The result obtained can be used as a decision support component for any educational system. The WEKA/Python software will be used to Estimate Education completion performance in educational institutions using Systematic Transformation of Asynchronous Regions (STAR) K* clustering algorithm.
Authors and Affiliations
S. N. Ali Ansari, Dr. Srinivasa Rao V, Dr. V. Srinivas
Keywords
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