An Integrated Classification Model for Financial Data Mining
This addresses performance issues in financial data mining for businesses, but appears incremental as it builds on existing techniques without specifying major breakthroughs.
The paper tackles the lack of general models for financial data analysis by presenting a new classification model, evaluating it on real-world data to demonstrate performance improvements.
Nowadays, financial data analysis is becoming increasingly important in the business market. As companies collect more and more data from daily operations, they expect to extract useful knowledge from existing collected data to help make reasonable decisions for new customer requests, e.g. user credit category, churn analysis, real estate analysis, etc. Financial institutes have applied different data mining techniques to enhance their business performance. However, simple ap-proach of these techniques could raise a performance issue. Besides, there are very few general models for both understanding and forecasting different finan-cial fields. We present in this paper a new classification model for analyzing fi-nancial data. We also evaluate this model with different real-world data to show its performance.