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Author Kovalerchuk, Boris.

Title Data mining in finance : advances in relational and hybrid methods / by Boris Kovalerchuk and Evgenii Vityaev
Published Boston ; London : Kluwer Academic, [2000]


Location Call no. Vol. Availability
 MELB  332.1028563 Kov/Dmi  UNAVAILABLE C19
Description xiv,308 pages : illustrations ; 25 cm
Series Kluwer international series in engineering and computer science ; SECS 547
Kluwer international series in engineering and computer science ; SECS 547
Contents Machine derived contents note: Foreword; G. Piatetsky-Shapiro. Preface. Acknowledgements. 1. The Scope and Methods of the Study. 2. Numerical Data Mining Models with Financial Applications. 3. Rule-Based and Hybrid Financial Data Mining. 4. Relational Data Mining (RDM). 5. Financial Applications of Relational Data Mining. 6. Comparison of Performance of RDM and other methods in financial applications. 7. Fuzzy logic approach and its financial applications. References. Subject Index
Summary "Data Mining in Finance presents a comprehensive overview of major algorithmic approaches to predictive data mining, including statistical, neural networks, rule-based, decision-tree, and fuzzy-logic methods, and then examines the suitability of these approaches to financial data mining. The book focuses specifically on relational data mining (RDM), which is a learning method able to learn more expressive rules than other symbolic approaches." "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."--Jacket
Analysis Samfundsvidenskab Økonomi
Bibliography Includes bibliographical references and index
Notes Also available in print
Mode of access: World Wide Web
Print version record
Subject Data mining.
Finance -- Data processing.
Investments -- Data processing.
Stock price forecasting -- Data processing.
Author Vityaev, Evgenii.
LC no. 00022043
ISBN 0792378040 (acid-free paper)