An Integrated Classification Model for Financial Data Mining

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.

[1]  J. A. Hartigan,et al.  A k-means clustering algorithm , 1979 .

[2]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[3]  Tao Guo,et al.  Neural data mining for credit card fraud detection , 2008, 2008 International Conference on Machine Learning and Cybernetics.

[4]  Ingoo Han,et al.  Hybrid neural network models for bankruptcy predictions , 1996, Decis. Support Syst..

[5]  Deborah R. Carvalho,et al.  A hybrid decision tree/genetic algorithm method for data mining , 2004, Inf. Sci..

[6]  Donald W. Bouldin,et al.  A Cluster Separation Measure , 1979, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  金田 重郎,et al.  C4.5: Programs for Machine Learning (書評) , 1995 .

[8]  Carl E. Rasmussen,et al.  Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.

[9]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[10]  Daniel Sánchez,et al.  ART: A Hybrid Classification Model , 2004, Machine Learning.

[11]  Andreas S. Weigend,et al.  Data Mining in Finance: Report from the Post-Nncm-96 Workshop on Teaching Computer Intensive Methods for Financial Modeling and Data Analysis , 1997 .

[12]  Zikrija Avdagic,et al.  On-line evolving clustering for financial statements' anomalies detection , 2009, 2009 XXII International Symposium on Information, Communication and Automation Technologies.

[13]  Vijay K Chaudhari,et al.  Neural network learning improvement using K-means clustering algorithm to improve the performance of web traffic mining , 2011, 2011 3rd International Conference on Electronics Computer Technology.

[14]  D. J. Newman,et al.  UCI Repository of Machine Learning Database , 1998 .

[15]  Ingoo Han,et al.  Hybrid genetic algorithms and support vector machines for bankruptcy prediction , 2006, Expert Syst. Appl..

[16]  J. Ross Quinlan.  Learning First-Order Definitions of Functions , 1996, J. Artif. Intell. Res..

[17]  Catherine Blake,et al.  UCI Repository of machine learning databases , 1998 .

[18]  Martin T. Hagan,et al.  Neural network design , 1995 .

[19]  Huan Liu,et al.  Toward integrating feature selection algorithms for classification and clustering , 2005, IEEE Transactions on Knowledge and Data Engineering.

[20]  Inderjit S. Dhillon,et al.  A Data-Clustering Algorithm on Distributed Memory Multiprocessors , 1999, Large-Scale Parallel Data Mining.

[21]  Halima Bensmail,et al.  Analyzing Imputed Financial Data: A New Approach to Cluster Analysis , 2004 .

[22]  David Heckerman,et al.  Bayesian Networks for Knowledge Discovery , 1996, Advances in Knowledge Discovery and Data Mining.

[23]  Daniel T. Larose,et al.  Discovering Knowledge in Data: An Introduction to Data Mining , 2005 .