Data Mining in Finance


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Data Mining in Finance: Advances in Relational and Hybrid Methods
by Boris Kovalerchuk and Evgenii Vityaev,
Kluwer Acad. Publ, 2000

The Kluwer International Series in Engineering and Computer Science , Vol.  547  

ISBN: 0-7923-7804-0
Kluwer's prepublication flyer, 2000 (pdf)

Foreword by Gregory Piatetsky-Shapiro

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

Getting the book:   

2. Numerical Data Mining Models with Financial Applications


  • Statistical, autoregression models
  • Financial applications of autoregression models
  • Instance-based learning and financial applications
  • Neural networks
  • Neural networks and hybrid systems in finance
  • Recurrent neural networks in finance
  • Modular networks and genetic algorithms
  • Testing results and the complete round robin method
  • Expert mining
  • Interactive learning of monotone Boolean
  • functions