We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 December 2024
Reseach Article

Application of Association Rule Mining to Help Determine the Process of Career Selection

by Harini Peri, Preetham Kumar
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 94 - Number 16
Year of Publication: 2014
Authors: Harini Peri, Preetham Kumar
10.5120/16442-6052

Harini Peri, Preetham Kumar . Application of Association Rule Mining to Help Determine the Process of Career Selection. International Journal of Computer Applications. 94, 16 ( May 2014), 15-19. DOI=10.5120/16442-6052

@article{ 10.5120/16442-6052,
author = { Harini Peri, Preetham Kumar },
title = { Application of Association Rule Mining to Help Determine the Process of Career Selection },
journal = { International Journal of Computer Applications },
issue_date = { May 2014 },
volume = { 94 },
number = { 16 },
month = { May },
year = { 2014 },
issn = { 0975-8887 },
pages = { 15-19 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume94/number16/16442-6052/ },
doi = { 10.5120/16442-6052 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:18:15.367557+05:30
%A Harini Peri
%A Preetham Kumar
%T Application of Association Rule Mining to Help Determine the Process of Career Selection
%J International Journal of Computer Applications
%@ 0975-8887
%V 94
%N 16
%P 15-19
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The enormous data present at a university can be analyzed to generate useful information regarding the career paths chosen by students over the last few years. This information can not only be used by the students for analyzing the scope of their chosen career path but also by various authorities in analyzing the present career trends and understanding the scope of improvement among the less chosen ones. Dynamic Itemset Counting algorithm is an Association Rule Mining Technique used to identify patterns from an enormous amount of data, such as the data present at a university's repository. This model is an attempt towards uncovering hidden patterns. The generated results of the algorithm help in giving useful insights to decision makers in helping them make better and informed decisions.

References
  1. Anubha Sharma, Nirupma Tivari . 2012. "A Survey of Association Rule Mining Using Genetic Algorithm. " A Survey of Association Rule Mining Using Genetic Algorithm. Volume 1, Issue 2, pp 5-11.
  2. Bakar, Z. A. et al. 2006. "A Comparative Study for Outlier Detection Techniques in Data Mining. " IEEE Conference on Cybernetics and Intelligent Systems. Bangkok, 7-9 June 2006. IEEE, pp 1-6.
  3. Ila Chandrakar, and Mari Kirthima, A. 2013. 'A Survey on Association Rule Mining Algorithms. " International Journal of Mathematics and Computer Research. Volume 1, Issue 10, pp 270-272.
  4. Sabarigirivason, K. et al. 2014. Association Rule Mining Based a Personalized Mobile Search Engine. ' International Journal of Advanced Research in Computer and Communication Engineering. Volume 3, Issue 1, pp 5272-5278.
  5. Mrs. Sonali Manoj Raut, Prof. Dhananjay Dakhane. 2012. "Comparative Study of Clustering and Association Method for Large Database in Time Domain. " International Journal of Advanced Research in Computer Science and Software Engineering. Volume 2, Issue 12.
  6. Numprasertchai, S. Poovaravan, Y. 2006. "Enhancing University Competitiveness through ICT Based Knowledge Management Systems. " IEEE Int. Conf. on Management of Innovation & Technology. Volume-1, pp 417–421,IEEE[Online]. DOI: 10. 1109/ ICMIT. 2006. 262196.
  7. Oded Maimon, Lior Rokach. 2005. "Introduction to Knowledge Discovery in Databases. " Data Mining and Knowledge Discovery Handbook, pp 1-17. Springer US [Online]. Available at: http://link. springer. com.
  8. Preeti Paranjape-Voditel, Dr. Umesh Deshpande. 2011. "A DIC-based Distributed Algorithm for Frequent Itemset Generation. " Journal of Software. Volume 6, Issue 2, pp 306-313.
  9. Qiankun Zhao, Nanyang Technological University, Singapore and Sourav, S. Bhowmick. 2003. "Technical Report on Association Rule Mining": A Survey, No. 2003116. CAIS, Nanyang Technological University, Singapore.
  10. Rakesh Agrawal, Tomasz Imielinski, Arun, Swami, N. 1993. "Mining association rules between sets of items in large databases". Proceedings of the 1993 ACM SIGMOD International Conference on Management , pp 207-216. Washington DC, USA.
  11. Sergey Brin et al. 1997. "Dynamic Itemset Counting and Implication Rules for Market Basket Data". ACM SIGMOD International Conference on Management of Data,pp255-264[Online]. DOI:10. 1145/ 253262. 253325.
  12. Sohil, Pandya. D and D. P. V. V. 2012. "Studying in impact of past performance in academics using data mining technique". International Journal of Information and Computing Technology.
  13. Tannu Arora, Rahul Yadav. 2011. "Improved Association Mining Algorithm for Large Dataset. " IJCEM International Journal of Computational Engineering & Management, pp 33-38. ISSN [Online]. Available at: http://www. ijcem. org.
Index Terms

Computer Science
Information Sciences

Keywords

Preferred attribute support confidence minimum support dynamic itemset counting algorithm