£143.36

Springer Data Mining in Finance - Relational and Hybrid Methods

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Description

Explore advanced predictive techniques in the financial sector with Data Mining in Finance. This volume from The Springer International Series in Engineering and Computer Science provides a detailed examination of major algorithmic approaches used in modern financial analysis. Readers can study a wide range of methodologies, including statistical models, neural networks, rule-based systems, decision trees, and fuzzy-logic methods. The text evaluates how these different approaches perform when applied specifically to financial data mining tasks. A primary focus of this work is Relational Data Mining (RDM). Unlike other symbolic approaches, RDM is a learning method capable of generating more expressive rules. This makes it highly effective for financial mining because it utilizes underlying domain knowledge more efficiently. This book serves as a technical resource for understanding how hybrid and relational methods can improve predictive accuracy and rule expression in complex financial environments.

Key Features

Covers major algorithmic approaches to predictive data mining including neural networks and statistical methods.

Provides a detailed examination of decision-tree, rule-based, and fuzzy-logic techniques.

Focuses on Relational Data Mining (RDM) to help learn more expressive rules than standard symbolic approaches.

Evaluates the specific suitability of various mining methods when applied to financial data sets.

Demonstrates how RDM makes better use of underlying domain knowledge for improved financial mining results.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
30 April 2000
Listed Since
15 February 2007

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No barcode data available

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