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Improved Frequent Pattern Mining for Educational Data by using Mapreduce Approach in Hadoop

by Than Htike Aung, Nang Saing Moon Kham, Soe Soe Mon
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 175 - Number 37
Year of Publication: 2020
Authors: Than Htike Aung, Nang Saing Moon Kham, Soe Soe Mon
10.5120/ijca2020920935

Than Htike Aung, Nang Saing Moon Kham, Soe Soe Mon . Improved Frequent Pattern Mining for Educational Data by using Mapreduce Approach in Hadoop. International Journal of Computer Applications. 175, 37 ( Dec 2020), 13-20. DOI=10.5120/ijca2020920935

@article{ 10.5120/ijca2020920935,
author = { Than Htike Aung, Nang Saing Moon Kham, Soe Soe Mon },
title = { Improved Frequent Pattern Mining for Educational Data by using Mapreduce Approach in Hadoop },
journal = { International Journal of Computer Applications },
issue_date = { Dec 2020 },
volume = { 175 },
number = { 37 },
month = { Dec },
year = { 2020 },
issn = { 0975-8887 },
pages = { 13-20 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume175/number37/31691-2020920935/ },
doi = { 10.5120/ijca2020920935 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:40:30.758449+05:30
%A Than Htike Aung
%A Nang Saing Moon Kham
%A Soe Soe Mon
%T Improved Frequent Pattern Mining for Educational Data by using Mapreduce Approach in Hadoop
%J International Journal of Computer Applications
%@ 0975-8887
%V 175
%N 37
%P 13-20
%D 2020
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, we describe the formatting guidelines for IJCA Journal Submission. In the education area of Myanmar, computers, mobile and internet have become important tools for high school students. To enable the quality and the flexibility of the education, verities of education programs and methods are greatly included but with different manners. In this paper, the field of large educational data and how big educational data can be analysis to provided quality improvement in education. For the frequent pattern mining and exploitation of educational data, proposed system present improved data mining techniques and popular applied hadoop mapreduce for large data manipulation such as parallel processing data analysis such as learning, academic and visual analytics, providing examples of how these techniques and methods could be used. The proposed system has been started pay attention to the teacher assessment application of data and data analytics to handle large data generated in the educational sector. These data stored in Hadoop file system, then discover frequent pattern by using mapreduce support apriori, eclat and prefix tree methods. These approached is effective and scalable for large data instead use of traditional standard data mining tools.

References
  1. B. Singh, R. Singh,N. Kushwaha, O. P. Vyas, "An Efficient Approach for Discovering Closed Frequent Patterns in High Dimensional Data Sets",Advanced Computing, Networking and Informatics- Volume 1 pp 519-528
  2. LESKOVEC, Jure; RAJARAMAN, Anand; ULLMAN, Jeffrey D. Mining of Massive Datasets, 2nd Ed. Cambridge University Press, 2014. ISBN 978-1107077232.
  3. GORUNESCU, Florin. Data Mining - Concepts, Models and Techniques.
  4. Springer, 2011. Intelligent Systems Reference Library. ISBN 978-3- 642-19720-8. Available from DOI: 10.1007/978-3-642-19721-5.
  5. AGGARWAL, Charu C.; HAN, Jiawei (eds.). Frequent Pattern Mining.Springer, 2014. ISBN 978-3-319-07820-5. Available from DOI: 10 . 1007/978-3-319-07821-2.
  6. ALVAREZ, Sergio A. Chi-squared computation for association rules: preliminary result. In: Technical Report BC-CS-2003-01. 2003. Avail- able also from: http : / / www . cs . bc . edu / ~alvarez / ChiSquare / chi2tr.pdf.
  7. AGRAWAL, Rakesh; SRIKANT, Ramakrishnan. Fast Algorithms for Mining Association Rules in Large Databases. In: VLDB’94, Proceed- ings of 20th International Conference on Very Large Data Bases, Septem- ber 12-15, 1994, Santiago de Chile, Chile. 1994, pp. 487–499. Available also from: http://www.vldb.org/conf/1994/P487.PDF.
  8. AGRAWAL, Rakesh; IMIELINSKI, Tomasz; SWAMI, Arun N. Mining Association Rules between Sets of Items in Large Databases. In: Pro- ceedings of the 1993 ACM SIGMOD International Conference on Man- agement of Data, Washington, D.C., May 26-28, 1993. 1993, pp. 207– 216. Available from DOI: 10.1145/170035.170072.
  9. ZAKI, Mohammed Javeed; GOUDA, Karam. Fast vertical mining us- ing diffsets. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 24 - 27, 2003. 2003, pp. 326–335. Available from DOI: 10.1145/956750.956788.
  10. AGARWAL, Ramesh C.; AGGARWAL, Charu C.; PRASAD, V. V. V.Depth first generation of long patterns. In: Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining, Boston, MA, USA, August 20-23, 2000. 2000, pp. 108–118. Available from DOI: 10.1145/347090.347114.
  11. HAN, Jiawei; PEI, Jian; YIN, Yiwen. Mining Frequent Patterns with- out Candidate Generation. In: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, May 16-18, 2000, Dal- las, Texas, USA. 2000, pp. 1–12. Available from DOI: 10.1145/342009. 335372.
  12. ZAKI, Mohammed Javeed; PARTHASARATHY, Srinivasan; OGI- HARA, Mitsunori; LI, Wei. New Algorithms for Fast Discovery of Association Rules. In: Proceedings of the Third International Conference on Knowledge Discovery and Data Mining (KDD-97), Newport Beach, California, USA, August 14-17, 1997. 1997, pp. 283–286. Available also from: http://www.aaai.org/Library/KDD/1997/kdd97-060.php.
  13. ZAKI, Mohammed Javeed. Scalable Algorithms for Association Min- ing. IEEE Trans. Knowl. Data Eng. 2000, vol. 12, no. 3, pp. 372–390. Available from DOI: 10.1109/69.846291.
  14. LIN, Goh Chun; DESMOND, Koh Eng Tat; HTOON, Naing Tayza; THUAT, NV. A Fresh Graduate’s Guide to Software Development Tools and Technologies. Chapter-6: Scalability, School of Computing, National University of Singapore. 2012.
  15. DEAN, Jeffrey; GHEMAWAT, Sanjay. MapReduce: Simplified Data Processing on Large Clusters. In: 6th Symposium on Operating Sys- tem Design and Implementation (OSDI 2004), San Francisco, California, USA, December 6-8, 2004. 2004, pp. 137–150. Available also from: http://www.usenix.org/events/osdi04/tech/dean.html.
  16. HURWITZ, Judith; NUGENT, Alan; HALPER, Dr. Fern; KAUFMANN,Marcia. Big Data for Dummies. John Wiley & Sons, Inc., 2013. ISBN 978-1-118-50422-2.
  17. SAVASERE, Ashok; OMIECINSKI, Edward; NAVATHE, Shamkant B. An Efficient Algorithm for Mining Association Rules in Large Databases. In: VLDB’95, Proceedings of 21th International Conference on Very Large Data Bases, September 11-15, 1995, Zurich, Switzerland. 1995, pp. 432–444. Available also from: http : / / www . vldb . org / conf/1995/P432.PDF.
  18. A. Tabarcea, V. Hautamäki, P. Fränti,”AD-HOC GEOREFERENCING OF WEB-PAGES USING STREET-NAME PREFIX TREES”, 6th International Conference on Web Information Systems and Technologies,April-2010, DOI: 10.1007/978-3-642-22810-0_19 ·
Index Terms

Computer Science
Information Sciences

Keywords

Hadoop Mapreduce Eclat Apriori Prefix

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