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Association Rule Mining of Classified and Clustered Data of e-Learning System

Published on March 2012 by Sunita B Aher, LOBO L.M.R.J.
International Conference and Workshop on Emerging Trends in Technology
Foundation of Computer Science USA
ICWET2012 - Number 10
March 2012
Authors: Sunita B Aher, LOBO L.M.R.J.
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Sunita B Aher, LOBO L.M.R.J. . Association Rule Mining of Classified and Clustered Data of e-Learning System. International Conference and Workshop on Emerging Trends in Technology. ICWET2012, 10 (March 2012), 10-17.

@article{
author = { Sunita B Aher, LOBO L.M.R.J. },
title = { Association Rule Mining of Classified and Clustered Data of e-Learning System },
journal = { International Conference and Workshop on Emerging Trends in Technology },
issue_date = { March 2012 },
volume = { ICWET2012 },
number = { 10 },
month = { March },
year = { 2012 },
issn = 0975-8887,
pages = { 10-17 },
numpages = 8,
url = { /proceedings/icwet2012/number10/5384-1075/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference and Workshop on Emerging Trends in Technology
%A Sunita B Aher
%A LOBO L.M.R.J.
%T Association Rule Mining of Classified and Clustered Data of e-Learning System
%J International Conference and Workshop on Emerging Trends in Technology
%@ 0975-8887
%V ICWET2012
%N 10
%P 10-17
%D 2012
%I International Journal of Computer Applications
Abstract

Classification is the supervised learning algorithm that maps the data into predefined groups & classes. Clustering is unsupervised algorithm which finds groups of objects such that the objects in one group will be similar to one another and different from the objects in another group while Association Rule algorithms are used to show the relationship between the data items. In this paper we propose the combination of three data mining algorithms: ADTree classification algorithm, Simple K-means Clustering Algorithm & Apriori Association Rule algorithm to recommend the course selected by the student. Here we consider the real sample data of Moodle courses of our college & check the result using the open source data mining tool Weka. First we classify the data using ADTree classification algorithm & then we apply the Simple k-means algorithm to the resultant data to obtain clusters. We apply the Apriori Association Rule algorithm on clusters obtained to find the best combination of courses which gives the better result as compare to result we obtained using only the Apriori Association Rule.

References
  1. Alexandros Nanopoulos, Apostolos N. Papadopoulos, Yannis Manolopoulos:” Mining association rules in very large clustered domains” Information Systems 32 (2007) 649–669
  2. Mazid, M.M. Shawkat Ali, A.B.M. Tickle, K.S. : “Finding a unique Association Rule Mining algorithm based on data characteristics”
  3. Aman Kumar Sharma, Suruchi Sahni:” A Comparative Study of Classification Algorithms for Spam Email Data Analysis” in International Journal of computer science & Engineering.
  4. Daniel Sánchez, Jose-María Serrano, Amparo Vila, Fernando Berzal, Juan-Carlos Cubero, Nicolás Marín:”Association rule evaluation for classification purposes” TAMIDA2005, pp.135-144 ISBN: 84-9732-449-8 © 2005
  5. B.Ramasubbareddy, A.Govardhan & A.Ramamohanreddy:”Classification Based on Positive and Negative Association Rules” International Journal of Data Engineering, (IJDE), Volume (2): Issue (2) : 2011 84
  6. Lili He, Hongtao Bai:”Aspect Mining Using Clustering and Association Rule Method” IJCSNS International Journal of Computer Science and Network Security, VOL.6 No.2A, February 2006
  7. “Data Mining Introductory and Advanced Topics” by Margaret H. Dunham
  8. Sunita B Aher and Lobo L.M.R.J. Data Mining in Educational System using WEKA. IJCA Proceedings on International Conference on Emerging Technology Trends (ICETT) (3):20-25, 2011. Published by Foundation of Computer Science, New York, USA (ISBN: 978-93-80864-71-13)
  9. Sunita B Aher and Lobo L.M.R.J.: “Preprocessing Technique for Association Rule Based Course Recommendation System in E-learning” selected in ICECT-2012, proceeding will published by IEEE.
  10. Sunita B Aher and Lobo L.M.R.J. Article: A Framework for Recommendation of courses in E-learning System. International Journal of Computer Applications 35(4):21-28, December 2011. Published by Foundation of Computer Science, New York, USA ISSN 0975 – 8887
Index Terms

Computer Science
Information Sciences

Keywords

Weka ADTree Classification Algorithm Simple K-means Algorithm Apriori Association Rule Algorithm Learning management System