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Title:
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SELF-ORGANIZING DATA MINING USING ENHANCEDGROUP METHOD DATA HANDLING APPROACH |
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Author(s):
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Godfrey C. Onwubolu , Petr Buryan , Ajith Abraham |
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ISBN:
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978-972-8924-40-9 |
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Editors:
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Jörg Roth, Jairo Gutiérrez and Ajith P. Abraham (series editors: Piet Kommers, Pedro Isaías and Nian-Shing Chen) |
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Year:
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2002 |
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Edition:
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Single |
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Type:
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Short Paper |
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First Page:
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170 |
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Last Page:
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175 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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Data Mining (DM) is a relatively recent technology that is employed in inferring useful knowledge that can be put to use
from a vast amount of data. This paper presents the data mining processes applied to the seemingly chaotic behavior of
stock markets which could be well represented using the enhance GMDH, and we compared its results with published
results using neural network, TS fuzzy system and hierarchical TS fuzzy techniques. To demonstrate the capabilities of
the different techniques, we considered Nasdaq-100 index of Nasdaq Stock MarketSM and the S&P CNX NIFTY stock
index. We analyzed 7 year's Nasdaq 100 main index values and 4 year's NIFTY index values. This paper investigates the
development of novel reliable and efficient techniques to model the seemingly chaotic behavior of stock markets.
Experimental results reveal that all the models considered could represent the stock indices behavior very accurately and
that the proposed e-GMDH approach is a useful for data mining technique for forecasting and modeling stock indices. |
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