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Future Trend Prediction of Indian IT Stock Market using Association Rule Mining of Transaction data

by Rajesh V. Argiddi, S. S. Apte
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 39 - Number 10
Year of Publication: 2012
Authors: Rajesh V. Argiddi, S. S. Apte
10.5120/4858-7132

Rajesh V. Argiddi, S. S. Apte . Future Trend Prediction of Indian IT Stock Market using Association Rule Mining of Transaction data. International Journal of Computer Applications. 39, 10 ( February 2012), 30-34. DOI=10.5120/4858-7132

@article{ 10.5120/4858-7132,
author = { Rajesh V. Argiddi, S. S. Apte },
title = { Future Trend Prediction of Indian IT Stock Market using Association Rule Mining of Transaction data },
journal = { International Journal of Computer Applications },
issue_date = { February 2012 },
volume = { 39 },
number = { 10 },
month = { February },
year = { 2012 },
issn = { 0975-8887 },
pages = { 30-34 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume39/number10/4858-7132/ },
doi = { 10.5120/4858-7132 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:26:06.250288+05:30
%A Rajesh V. Argiddi
%A S. S. Apte
%T Future Trend Prediction of Indian IT Stock Market using Association Rule Mining of Transaction data
%J International Journal of Computer Applications
%@ 0975-8887
%V 39
%N 10
%P 30-34
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The approach stated in this paper mainly focuses on minimizing the length of the transaction table of the stock market, based on some common features among the attributes which indirectly minimize the complexity involved in processing; we call this approach as Fragment Based Mining. This deals mainly with reducing the time and space complexity involved in processing the data. Experimentally we try to show our approach is promising one. We conclude that this approach can potentially be used for predictions and recommendations stock trading platforms.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Apriori FITI Fragment Based Mining Stock Data

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