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Combination of Clustering, Classification & Association Rule based Approach for Course Recommender System in E-learning

by Sunita B. Aher, Lobo L.M.R.J.
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 39 - Number 7
Year of Publication: 2012
Authors: Sunita B. Aher, Lobo L.M.R.J.
10.5120/4830-7087

Sunita B. Aher, Lobo L.M.R.J. . Combination of Clustering, Classification & Association Rule based Approach for Course Recommender System in E-learning. International Journal of Computer Applications. 39, 7 ( February 2012), 8-15. DOI=10.5120/4830-7087

@article{ 10.5120/4830-7087,
author = { Sunita B. Aher, Lobo L.M.R.J. },
title = { Combination of Clustering, Classification & Association Rule based Approach for Course Recommender System in E-learning },
journal = { International Journal of Computer Applications },
issue_date = { February 2012 },
volume = { 39 },
number = { 7 },
month = { February },
year = { 2012 },
issn = { 0975-8887 },
pages = { 8-15 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume39/number7/4830-7087/ },
doi = { 10.5120/4830-7087 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:25:48.086727+05:30
%A Sunita B. Aher
%A Lobo L.M.R.J.
%T Combination of Clustering, Classification & Association Rule based Approach for Course Recommender System in E-learning
%J International Journal of Computer Applications
%@ 0975-8887
%V 39
%N 7
%P 8-15
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Data mining also known as Knowledge Discovery in Database is the process of discovering new pattern from large data set. E-learning is the electronically learning & teaching process. Course Recommender System allows us to study the behavior of student regarding the courses. In Course Recommender System in E-learning, we collect the data regarding the student enrollments for a specific set of data i.e. the courses which the students like to learn. After collection of data, we apply three data mining techniques namely clustering, classification & association rule to find the best combination of courses. Here we compare the result of this combined approach with result obtained using only association rule & present how this combined approach is better than only the association rule algorithm.

References
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  3. Al¶³pio Jorge:”Hierarchical Clustering for thematic browsing and summarization of large sets of Association Rules” Supported by the POSI/SRI/39630/2001/Class Project
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  7. 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)
  8. Sunita B Aher and Lobo L.M.R.J.: “Preprocessing Technique for Association Rule Based Course Recommendation System in E-learning” selected in ICECT-12, proceeding published by IEEE
  9. “Data Mining Introductory and Advanced Topics” by Margaret H. Dunham
  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
  11. Alternating decision tree, available at: http://en.wikipedia.org/wiki/alternating_decision_tree Accessed on 13-02-2012
  12. Sunita B Aher and Lobo L.M.R.J.:” Mining Association Rule in Classified Data for Course Recommender System in E-Learning” Selected in International Journal of Computer Applications ((IJCA)) Published by Foundation of Computer Science, New York, USA ISSN 0975 – 8887
Index Terms

Computer Science
Information Sciences

Keywords

Weka Moodle Simple K-means Algorithm ADTree Classification Algorithm Apriori Association Rule Algorithm

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