Onwubolu, Godfrey C. and Buryan, P. and Abraham, Ajith (2007) Self organizing data mining using enhanced group method data handling approach. [Conference Proceedings]
Full text not available from this repository.Abstract
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.
| Item Type: | Conference Proceedings |
|---|---|
| Subjects: | A General Works > AI Indexes (General) |
| Divisions: | Faculty of Science, Technology and Environment (FSTE) > School of Engineering and Physics |
| Depositing User: | Ms Mereoni Camailakeba |
| Date Deposited: | 25 Apr 2007 07:26 |
| Last Modified: | 28 Jun 2012 09:20 |
| URI: | https://repository.usp.ac.fj/id/eprint/4276 |
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