£144.97

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£145 today · all-time low £143 (Sep 2026) · usually £145

Springer Data Mining in Finance: Advances in Relational Methods - Springer 547

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Description

Data Mining in Finance delivers a comprehensive exploration of predictive data mining techniques applied to financial markets and analysis. The book examines statistical methods, neural networks, rule-based systems, decision-tree approaches, and fuzzy-logic frameworks, then evaluates their effectiveness for financial data mining applications. The core focus centres on relational data mining (RDM), an advanced learning method that generates more expressive and sophisticated rules than conventional symbolic approaches. RDM proves particularly valuable for financial mining because it leverages underlying domain knowledge more effectively, enabling deeper insights from complex financial datasets. This Springer volume (547 in The International Series in Engineering and Computer Science) combines theoretical foundations with practical algorithmic approaches, making it essential for financial analysts, data scientists, and researchers seeking to apply modern data mining techniques to banking, investment, and risk management challenges.

Key Features

Relational data mining (RDM) methods enable learning of more expressive rules tailored specifically for complex financial data analysis.

The book covers statistical, neural network, rule-based, decision-tree, and fuzzy-logic approaches to predictive data mining in finance.

Springer's 547th volume integrates theoretical algorithmic knowledge with practical applications for financial professionals and researchers.

RDM leverages domain-specific financial knowledge more effectively than traditional symbolic learning methods.

Comprehensive overview of major algorithmic approaches designed to improve predictive accuracy in financial data mining projects.

Product Specifications

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

Barcode

No barcode data available

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