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Part of the book series: The Springer International Series in Engineering and Computer Science (SECS, volume 547)
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Table of contents (7 chapters)
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Front Matter
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Back Matter
About this book
Data Mining in Finance introduces a new approach, combining relational data mining with the analysis of statistical significance of discovered rules. This reduces the search space and speeds up the algorithms. The book also presents interactive and fuzzy-logic tools for `mining' the knowledge from the experts, further reducing the search space.
Data Mining in Finance contains a number of practical examples of forecasting S&P 500, exchange rates, stock directions, and rating stocks for portfolio, allowing interested readers to start building their own models. This book is an excellent reference for researchers and professionals in the fields of artificial intelligence, machine learning, data mining, knowledge discovery, and applied mathematics.
Authors and Affiliations
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Central Washington University, USA
Boris Kovalerchuk
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Institute of Mathematics, Russian Academy of Sciences, Russia
Evgenii Vityaev
Bibliographic Information
Book Title: Data Mining in Finance
Book Subtitle: Advances in Relational and Hybrid Methods
Authors: Boris Kovalerchuk, Evgenii Vityaev
Series Title: The Springer International Series in Engineering and Computer Science
DOI: https://doi.org/10.1007/b116453
Publisher: Springer New York, NY
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eBook Packages: Springer Book Archive
Copyright Information: Springer Science+Business Media New York 2000
Hardcover ISBN: 978-0-7923-7804-4Published: 30 April 2000
Softcover ISBN: 978-1-4757-7332-3Published: 20 March 2013
eBook ISBN: 978-0-306-47018-9Published: 11 December 2005
Series ISSN: 0893-3405
Edition Number: 1
Number of Pages: XVI, 308
Topics: Data Structures and Information Theory, Artificial Intelligence, Finance, general