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CORR
2002
Springer

Symbolic Methodology in Numeric Data Mining: Relational Techniques for Financial Applications

8 years 8 months ago
Symbolic Methodology in Numeric Data Mining: Relational Techniques for Financial Applications
Currently statistical and artificial neural network methods dominate in financial data mining. Alternative relational (symbolic) data mining methods have shown their effectiveness in robotics, drug design and other applications. Traditionally symbolic methods prevail in the areas with significant nonnumeric (symbolic) knowledge, such as relative location in robot navigation. At first glance, stock market forecast looks as a pure numeric area irrelevant to symbolic methods. One of our major goals is to show that financial time series can benefit significantly from relational data mining based on symbolic methods. The paper overviews relational data mining methodology and develops this techniques for financial data mining.
Boris Kovalerchuk, Evgenii Vityaev, H. Yusupov
Added 18 Dec 2010
Updated 18 Dec 2010
Type Journal
Year 2002
Where CORR
Authors Boris Kovalerchuk, Evgenii Vityaev, H. Yusupov
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