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General Information
    • ISSN: 2010-3700
    • Frequency: Bimonthly
    • DOI: 10.18178/IJMLC
    • Editor-in-Chief: Dr. Lin Huang
    • Executive Editor:  Ms. Cherry L. Chen
    • Abstracing/Indexing: Engineering & Technology Digital Library, Google Scholar, Crossref, ProQuest, Electronic Journals Library, DOAJ and EI (INSPEC, IET).
    • E-mail: ijmlc@ejournal.net
Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2013 Vol.3(1): 4-6 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2013.V3.260

Relevance of Data Mining Techniques in Edification Sector

S. Anupama Kumar and M. N. Vijayalakshmi
Abstract—The various data mining techniques like classification, clustering and relationship mining can be applied on educational data to study the behavior and performance of the students. This study will enable the learner and teaching community to grow. These techniques can also be combined with specific discovery models and distillation of data for human judgment to enhance the learning community. This paper explores the various approaches and techniques of data mining which can be applied on Educational data to build up a new environment so as to give the new predictions on the data.

Index Terms—Classification, clustering, discovery with models, EDM, prediction, relationship mining.

The authors are with the Department of M.C.A., R. V. College of Engineering, Bangalore, India (e-mail: kumaranu.0506@gmail.com, mnviju74@gmail.com).

[PDF]

Cite:S. Anupama Kumar and M. N. Vijayalakshmi, "Relevance of Data Mining Techniques in Edification Sector," International Journal of Machine Learning and Computing vol. 3, no. 1, pp. 4-6, 2013.

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