research-article

Efficient Graph Clustering Algorithm and its use in Prediction of Students Performance

Authors:

Jayalatchumy Dhanpal,

Thambidurai Perumal,

UdhayakumaranAuthors Info & Claims

ICIA-16: Proceedings of the International Conference on Informatics and Analytics

Article No.: 39, Pages 1 - 5

https://doi.org/10.1145/2980258.2980343

Published: 25 August 2016 Publication History

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Abstract

Big data refers to its massive unstructured information that comes from the increasing use of technologies. Analyzing and extracting this information is a great challenge including data clustering. In order to overcome this challenge and to maintain its scalability and performance, an efficient clustering algorithm and a framework is necessary. So, this paper presents a refined clustering algorithm based on power method. The algorithm is made to run on a hadoop cluster. We also present case studies to demonstrate the difference among the results of clusters. A detailed study is made on the student's academic performance of a particular department in our college focusing on the factors that affect their placement.

References

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Ali Seyed Shirkhorshidi et al. 2014. Big Data Clustering: A Review ICCSA 2014, Part V, LNCS 8583, (2014) pp. 707--720.

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Dartmouth, Numerical methods. Chapter 10.3 Power method for approximating eigen values, ENGS 91, Dartmouth College Hanover. New Hampshire.

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Eric J.Heller, Lev Kaplan, Frank Polimann, 2008. Inflationary dynamics for matrix eigen value problems, PNAS. 105, 22(June 2008), 7631--7635 doi/10.1073/pnas.0801047105.

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T. Sanjana et al. 2016.A Survey on Clustering Techniques for Big Data Mining. Indian Journal of Science and Technology. 9, 3 (January 2016)

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Chanchai Yadav et al. 2013. Algorithm and approaches to Handle large Data- A Survey. IJCSN International Journal of Computer Science and Network. 2, 3 (2013) ISSN 2277-5420

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A. Fahad, N. Alshatri, Z. Tari, A. ALAmri, A. Y. Zomaya, I. Khalil, F. Sebti and A. Bouras. 2014. A Survey of Clustering Algorithms for Big Data: Taxonomy & Empirical Analysis, IEEE transactions on emerging topics in computing (2014).

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S.Arora, I.Chana. 2014. A survey of clustering techniques for Big Data Analysis. Confluence the Next Generation Information Technology Summit Confluence, 5th International Conference-. IEEE (2014), pp. 59--65.

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A. S. Shirkhorshidi, S. Aghabozorgi, T. Y. Wah, and T. Herawan. 2014. Big Data Clustering: A Review. Computational Science and Its Applications--ICCSA (2014), pp. 707--720

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Beste Eren 2015. A K-Means Algorithm Application on Big Data in Proceedings of the World Congress on Engineering and Computer Science. WCECS 2015, (October. 2015), San Francisco, USA.

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Digital Library

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Chen,W.Y, Song, Y, Bai,H and Lin. C. 2011. Parallel spectral clustering in distributed systems, IEEE Transactions on Pattern Analysis and Machine Intelligence 33,3(2011) pp. 568--586.

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Frank Lin frank, William W. 2010 Power Iteration Clustering, International Conference on Machine Learning, Haifa, Israel. (2010).

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Dhanapal Jayalatchumy, Perumal Thambidurai. 2016. Inflated Power Iteration Clustering Algorithm to Optimize Convergence Using Lagrangian Constraint. Advances in Intelligent system and computing, Proceedings of 5th Computer Science Online Conference (CSOC 16)(2) pp. 227--238

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Srinath Perera and Thilina Gunarathne. 2013. Hadoop MapReduce Cookbook Packt Publishing First published: (February, 2013): 2250113 Packt Publishing Ltd. Livery PlaceUK. ISBN 978-1-84951-728-7

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http://www.cs.colorado.edu/~kena/classes/5448/s11/presentations/hadoop.pdf

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yelade, O. J. et al. 2010. Application of k-Means Clustering algorithm for prediction of Students' Academic Performance (IJCSIS) International Journal of Computer Science and Information Security. 7, 1(2010). ISSN 1947-5500

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Bindiya M Varghese Clustering Student Data to Characterize Performance Patterns (IJACSA) International Journal of Advanced Computer Science and Applications, Special Issue on Artificial Intelligence. 138--140.

Cited By

Jayalatchumy DArunKumar GSaravanan D(2023)A Novel Graph Clustering Algorithm with Enhanced Security using Power Method and Homomorphic Encryption2023 1st International Conference on Optimization Techniques for Learning (ICOTL)10.1109/ICOTL59758.2023.10435058(1-6)Online publication date: 7-Dec-2023
https://doi.org/10.1109/ICOTL59758.2023.10435058
Hellas AIhantola PPetersen AAjanovski VGutica MHynninen TKnutas ALeinonen JMessom CLiao SRößling GScharlau B(2018)Predicting academic performance: a systematic literature reviewProceedings Companion of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education10.1145/3293881.3295783(175-199)Online publication date: 2-Jul-2018
https://dl.acm.org/doi/10.1145/3293881.3295783
Zheng LLiao XJin H(2018)Efficient and Scalable Graph Parallel Processing With Symbolic ExecutionACM Transactions on Architecture and Code Optimization10.1145/317043415:1(1-25)Online publication date: 22-Mar-2018
https://dl.acm.org/doi/10.1145/3170434

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cover image ACM Other conferences

ICIA-16: Proceedings of the International Conference on Informatics and Analytics

August 2016

868 pages

ISBN:9781450347563

DOI:10.1145/2980258

Copyright © 2016 ACM.

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Published: 25 August 2016

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ICIA-16: International Conference on Informatics and Analytics

August 25 - 26, 2016

Pondicherry, India

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Cited By

Jayalatchumy DArunKumar GSaravanan D(2023)A Novel Graph Clustering Algorithm with Enhanced Security using Power Method and Homomorphic Encryption2023 1st International Conference on Optimization Techniques for Learning (ICOTL)10.1109/ICOTL59758.2023.10435058(1-6)Online publication date: 7-Dec-2023
https://doi.org/10.1109/ICOTL59758.2023.10435058
Hellas AIhantola PPetersen AAjanovski VGutica MHynninen TKnutas ALeinonen JMessom CLiao SRößling GScharlau B(2018)Predicting academic performance: a systematic literature reviewProceedings Companion of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education10.1145/3293881.3295783(175-199)Online publication date: 2-Jul-2018
https://dl.acm.org/doi/10.1145/3293881.3295783
Zheng LLiao XJin H(2018)Efficient and Scalable Graph Parallel Processing With Symbolic ExecutionACM Transactions on Architecture and Code Optimization10.1145/317043415:1(1-25)Online publication date: 22-Mar-2018
https://dl.acm.org/doi/10.1145/3170434

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