Data Mining in Finance: Advances in Relational and Hybrid Methods: 547 (The Springer International Series in Engineering and Computer Science, 547) - Image 1

£149.64

Springer Data Mining in Finance: Advances in Relational and Hybrid Methods: 547 (The Springer International Series in Engineering and Computer Science, 547)

Name: Data Mining in Finance: Advances in Relational and Hybrid Methods: 547 (The Springer International Series in Engineering and Computer Science, 547)
Brand: Springer
Price: 146.43 GBP
Availability: InStock

Banking

Price History & Forecast

Last 86 days • 86 data points (No recent data available)

Historical

Generating forecast...

Price Distribution

Price distribution over 86 days • 2 price levels

Days at Price

Current Price

Price Analysis

Most common price: £146 (85 days, 98.8%)

Price range: £146 - £150

Price levels: 2 different prices over 86 days

Description

Data Mining in Finance presents a comprehensive overview of major algorithmic approaches to predictive data mining, including statistical, neural networks, ruled-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. RDM is thus better suited for financial mining, because it is able to make greater use of underlying domain knowledge. Relational data mining also has a better ability to explain the discovered rules - an ability critical for avoiding spurious patterns which inevitably arise when the number of variables examined is very large. The earlier algorithms for relational data mining, also known as inductive logic programming (ILP), suffer from a relative computational inefficiency and have rather limited tools for processing numerical data. 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.

Product Specifications

Brand: Springer
Format: paperback
ASIN: 1475773323
Category: Books > Subjects > Business, Finance & Law > Professional Finance > Banking
Domain: Amazon UK
Publication Date: 20 March 2013
Listed Since: 20 September 2013

Barcode

No barcode data available

Springer Data Mining in Finance: Advances in Relational and Hybrid Methods: 547 (The Springer International Series in Engineering and Computer Science, 547)

Price History & Forecast

Price Distribution

Price Analysis

Description

Product Specifications

Barcode

Similar Products You Might Like

Data Mining in Finance: Advances in Relational and Hybrid Methods: 547 (The Springer International Series in Engineering and Computer Science, 547)

Advanced Data Mining Techniques

Novel Financial Applications of Machine Learning and Deep Learning: Algorithms, Product Modeling, and Applications: 336 (International Series in Operations Research & Management Science, 336)

John Wiley & Sons Data Mining and Machine Learning Applications

Intelligent Data Mining: Techniques and Applications: 5 (Studies in Computational Intelligence, 5)

Advanced Techniques in Knowledge Discovery and Data Mining (Advanced Information and Knowledge Processing)

Machine Learning in Finance: From Theory to Practice

Machine Learning in Finance: From Theory to Practice

Advanced Methods for Knowledge Discovery from Complex Data (Advanced Information and Knowledge Processing)

Predictive Analytics and Data Mining: Concepts and Practice with RapidMiner

Financial Data Analytics with Machine Learning, Optimization and Statistics (Wiley Finance)

Data Mining Methods and Applications (Discrete Mathematics and Its Applications)

Data Mining: The Textbook

Computational Intelligence in Economics and Finance (Advanced Information Processing)

Data Mining: Ein Überblick über Verfahren und Anwendungsfelder

Machine Learning and AI in Finance

Data Mining: Concepts, Models and Techniques: 12 (Intelligent Systems Reference Library, 12)

Data Mining: The Textbook

Artificial Intelligence and Data Mining Approaches in Security Frameworks: Advances and Challenges (Advances in Data Engineering and Machine Learning)

Using Artificial Neural Networks for Timeseries Smoothing and Forecasting: Case Studies in Economics: 979 (Studies in Computational Intelligence, 979)

Financial Decision Making Using Computational Intelligence: 70 (Springer Optimization and Its Applications, 70)

Modern Data Mining Algorithms in C++ and CUDA C: Recent Developments in Feature Extraction and Selection Algorithms for Data Science

Springer - Fundamentals of Computational Intelligence 652

Data Science and Predictive Analytics: Biomedical and Health Applications using R (The Springer Series in Applied Machine Learning)