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Reseach Article

An Optimized Approach to Analyze Stock market using Data Mining Technique

Published on None 2011 by Dattatray P. Gandhmal, Ranjeetsingh B. Parihar, Rajesh V. Argiddi
International Conference on Emerging Technology Trends
Foundation of Computer Science USA
ICETT2011 - Number 1
None 2011
Authors: Dattatray P. Gandhmal, Ranjeetsingh B. Parihar, Rajesh V. Argiddi
7ecc92fd-32fc-4ed3-a823-88c2271910d2

Dattatray P. Gandhmal, Ranjeetsingh B. Parihar, Rajesh V. Argiddi . An Optimized Approach to Analyze Stock market using Data Mining Technique. International Conference on Emerging Technology Trends. ICETT2011, 1 (None 2011), 38-42.

@article{
author = { Dattatray P. Gandhmal, Ranjeetsingh B. Parihar, Rajesh V. Argiddi },
title = { An Optimized Approach to Analyze Stock market using Data Mining Technique },
journal = { International Conference on Emerging Technology Trends },
issue_date = { None 2011 },
volume = { ICETT2011 },
number = { 1 },
month = { None },
year = { 2011 },
issn = 0975-8887,
pages = { 38-42 },
numpages = 5,
url = { /proceedings/icett2011/number1/3494-icett004/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Emerging Technology Trends
%A Dattatray P. Gandhmal
%A Ranjeetsingh B. Parihar
%A Rajesh V. Argiddi
%T An Optimized Approach to Analyze Stock market using Data Mining Technique
%J International Conference on Emerging Technology Trends
%@ 0975-8887
%V ICETT2011
%N 1
%P 38-42
%D 2011
%I International Journal of Computer Applications
Abstract

This paper basically deals with identifying frequent patterns from large amount of stock data. These frequent patterns are identified based on rise and fall of stock prices. We have two stages, in first stage we categorize the stock data based on zero growth, slow growth and fast growth using k-means algorithm. In second stage we use CIR algorithm to generate useful trends about the behavior of stock markets. The trend holds to interpret the present and predict the next stock price. Some item-set from sales data indicate market needs and can be used in forecasting which has great potential for decision making, market competition and strategic planning. The objective in this research is to identify or to predict the stock market from the viewpoint of investors. So the investors can invest their shares in the appropriate companies based on zero growth, slow growth and fast growth. These two stage mining process that is k-means and CIR algorithm can generate more useful item-set according to our analysis.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Zero Growth (ZG) Slow Growth (SG) Fast Growth (FG) clustering