Title
Data Mining for Financial Applications
Document Type
Book Chapter
Department or Administrative Unit
Computer Science
Publication Date
2005
Abstract
This chapter describes Data Mining in finance by discussing financial tasks, specifics of methodologies and techniques in this Data Mining area. It includes time dependence, data selection, forecast horizon, measures of success, quality of patterns, hypothesis evaluation, problem ID, method profile, attribute-based and relational methodologies. The second part of the chapter discusses Data Mining models and practice in finance. It covers use of neural networks in portfolio management, design of interpretable trading rules and discovering money laundering schemes using decision rules and relational Data Mining methodology.
Recommended Citation
Kovalerchuk B., Vityaev E. (2005) Data Mining for Financial Applications. In: Maimon O., Rokach L. (eds) Data Mining and Knowledge Discovery Handbook. Springer, Boston, MA. https://doi.org/10.1007/0-387-25465-X_57
Journal
Data Mining and Knowledge Discovery Handbook
Rights
© Springer Science+Business Media, Inc. 2005
Comments
This book chapter was originally published in Data Mining and Knowledge Discovery Handbook. The full-text chapter from the publisher can be found here.
Due to copyright restrictions, this article is not available for free download from ScholarWorks @ CWU.