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Zeevi, Neural Networks: Theory and Applications. San Diego, CA: Academic, Raymond T. Ng and Jiawei Han.
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Efficient and effective clustering methods for spatial data mining. In Proc.
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Data Warehousing & Mining
Weiss, M. Sholom and Indurkhya, Nitin. This work is licensed under a Creative Commons Attribution 4. The names and email addresses entered in this journal site will be used exclusively for the stated purposes of this journal and will not be made available for any other purpose or to any other party. Submission of the manuscript represents that the manuscript has not been published previously and is not considered for publication elsewhere.
Published Nov 21, Due to the advancement of technology in this digital era, academic institutions are bringing out graduates as well as generating enormous amounts of data from their systems. Hidden information and hidden patterns in large datasets can be efficiently analyzed with data mining techniques.
Application of data mining techniques improves the performance of many organizational domains and the concept can be applied in the education sectors for their performance evaluation and improvement. This paper discusses how application of data mining can help the higher education institutions by enabling better understanding of the student data and focuses to consolidate clustering algorithms as applied in the context of educational data mining.
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Keywords: Data Mining, Educational Data Mining, clustering, cluster analysis, performance, evaluation, prediction. Decision Support Using Data Mining. Financial Times Pitman Baker, R. Milan: Springer, Bradshaw, J. P Douglas H. Data Mining: Concepts and Techniques, 3rd ed, J.
Han and M.
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Publishers, London R. Downloads Download data is not yet available. How to Cite. Job, M. European Journal of Engineering Research and Science.