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A Comparative Study of Association Rule Algorithms for Investment in Related Sector of Stock Market

by Rajeev Kumar, Arvind Kalia
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 62 - Number 10
Year of Publication: 2013
Authors: Rajeev Kumar, Arvind Kalia
10.5120/10119-4793

Rajeev Kumar, Arvind Kalia . A Comparative Study of Association Rule Algorithms for Investment in Related Sector of Stock Market. International Journal of Computer Applications. 62, 10 ( January 2013), 32-37. DOI=10.5120/10119-4793

@article{ 10.5120/10119-4793,
author = { Rajeev Kumar, Arvind Kalia },
title = { A Comparative Study of Association Rule Algorithms for Investment in Related Sector of Stock Market },
journal = { International Journal of Computer Applications },
issue_date = { January 2013 },
volume = { 62 },
number = { 10 },
month = { January },
year = { 2013 },
issn = { 0975-8887 },
pages = { 32-37 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume62/number10/10119-4793/ },
doi = { 10.5120/10119-4793 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:11:27.772377+05:30
%A Rajeev Kumar
%A Arvind Kalia
%T A Comparative Study of Association Rule Algorithms for Investment in Related Sector of Stock Market
%J International Journal of Computer Applications
%@ 0975-8887
%V 62
%N 10
%P 32-37
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Investment in the related stocks in share market plays vital role for investors. Variation in stock price is the barometer of growth of companies/sectors. Association Rule mining is one of the fundamental research topics in data mining and knowledge discovery that identifies interesting relationships between itemsets and predicted the associative and correlative behaviour for new data. In the present study the data of different stocks from National Stock Exchange of India Limited has taken and tried to find out the related stocks through Weka 3. 6. 5 data mining tool. In this paper four association rule algorithms: Apriori Association Rule, Predictive Apriori Association Rule, Tertius Association Rule and Filtered Associator were considered and the results of these four algorithms presented at different support and confidence level. It was found that Apriori Association Rule provided better results than other algorithms for selection of related stocks for investment in share market.

References
  1. Pang Ning Tan, Michael Steinbach, Vipin Kumar, 2009. Introduction to Data Mining, Pearson Education, pp. 223.
  2. Li Xiaohong, Huang Jingwei, "SHC: a spectral algorithm for hierarchical clustering", International Conference on "Multimedia Information Networking and Security", 2009.
  3. David W. Cheung, H. Y. Hwang ADA W FU, Jiawei Han, "Efficient Rule-based Attribute-oriented Induction for Data Mining", Journal of intelligent information system, 15,175-200,2000.
  4. A B M Shawkat Ali, Saleh A. Wasimi, 2009. Data Mining: Methods and Techniques, Cengage Learning, 2009 pp(172,175).
  5. Sunita B Aher and Lobo L. M. R. J, "A Comparative Study of Association Rule Algorithms for Course Recommender System in E-Learning" in International Journal of Computer Applications (0975-8887), Volume 39 – No. 1, February2012.
  6. Dennis P. Groth, Edward L. Robertson, "Discovering Frequent Itemsets in the Presence of Highly Frequent Items", Computer Science, Indiana University, Bloomington, IN 47405, USA.
  7. Agrawal Rakesh, Imienski Tmasz; Swami Arun, "Mining Association Rules Between Sets of Items in Large Databases", SIGMOD Conference: 207-216, 1993.
  8. Chia-Chia Lin, Dong-Her Shih, "Associating Information Literacy with Regulating Rules in Family by Data Mining", The 3rd International Conference on Innovative Computing Information and Control, IEEE-2008.
  9. He Lijun Li, Linghua Li, Xiaoniu Wang Degao, "Comparison and Analysis of algorithms for Association Rules", First International Workshop on Database Technology and Applications, 2009.
  10. Zijian Zheng, Ron Kohavi, Llew Mason, "Real world performance of association rule algorithms", 2001.
  11. Jiawei Han, Micheline Kamber, Jian Pei, 2012. Data Mining Concepts and Techniques, Morgan Kauffman Publishers, pp(244).
  12. Margaret H. Dunham, 2002. Data Mining Introductory and Advanced Topics, Prentice Hall.
  13. P. A. Flach, N. Lachiche, "Confirmation-Guided Discovery of first-order rules with Tertius". Machine Learning, 42:61-95, 2001
  14. http://www. nseindia. com/global/content/about_us/about_us. htm accessed on 20-09-2012
  15. http://www. cs. waikato. ac. nz/~ml/weka/ accessed on 21-09-2012.
Index Terms

Computer Science
Information Sciences

Keywords

Weka Association Rule mining Confidence level Support level

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