Data mining in finance : advances in relational and hybrid methods /

"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 bo...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Kovalerchuk, Boris
Otros Autores: Vityaev, Evgenii
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Boston : Norwell, Mass : Kluwer Academic Publishers ; Distributors for North, Central, and South America, Kluwer Academic Publishers, ©2000.
Colección:Kluwer international series in engineering and computer science ; SECS 547.
Temas:
Investments > Data processing.
Stock price forecasting > Data processing.
Data mining.
Investissements > Informatique.
Actions (Titres de société) > Prix > Prévision > Informatique.
Exploration de données (Informatique)
BUSINESS & ECONOMICS > Banks & Banking.
Data mining
Investments > Data processing
Stock price forecasting > Data processing
Samfundsvidenskab > Økonomi.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • The scope and methods of the study
  • Numerical data mining models with financial applications
  • Rule-based and hybrid financial data mining
  • Relational data mining (RDM)
  • Financial applications of relational data mining
  • Comparison of performance of RDM and other methods in financial applications
  • Fuzzy logic approach and its financial applications.

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