Skip to main content

An Integrated Model for Financial Data Mining

  • Conference paper
Multi-disciplinary Trends in Artificial Intelligence (MIWAI 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7694))

Abstract

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 approach of these techniques could raise a performance issue. Besides, there are very few general models for both understanding and forecasting different financial fields. We present in this paper an integrated model for analyzing financial data. We also evaluate this model with different real-world data to show its performance.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Brause, R., Langsdorf, T., Hepp, M.: Neural Data Mining for Credit Card Fraud Detection. Paper Presented at the Proceedings of the 11th IEEE International Conference on Tools with Artificial Intelligence (1999)

    Google Scholar 

  2. Huang, Y., Huang, B., Kechadi, M.-T.: A Rule-Based Method for Customer Churn Prediction in Telecommunication Services. In: Huang, J.Z., Cao, L., Srivastava, J. (eds.) PAKDD 2011, Part I. LNCS, vol. 6634, pp. 411–422. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  3. Weigend, A.: Data Mining in Finance: Report from the Post-NNCM-96 Workshop on Teaching Computer Intensive Methods for Financial Modeling and Data Analysis. In: Fourth International Conference on Neural Networks in the Capital Markets, NNCM 1996, pp. 399–411 (1997)

    Google Scholar 

  4. Tan, P.-N., Steinbach, M., Kumar, V.: Introduction to Data Mining, pp. 150–172. Addison Wesley (2006)

    Google Scholar 

  5. Quinlan, J.R.: Learning First-Order Definitions of Functions. Journal of Artificial Intelligence Research 5, 139–161 (1996)

    MATH  Google Scholar 

  6. Wong, B.K., Bodnovich, T.A., Selvi, Y.: Neural network applications in business: a review and analysis of the literature (1988-1995). Decis. Support Syst. 19(4), 301–320 (1997), doi:10.1016/s0167-9236(96)00070-x

    Article  Google Scholar 

  7. Cristianini, N., Taylor, J.-S.: An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge University Press (2000)

    Google Scholar 

  8. Cover, T.M., Hart, P.E.: Nearest Neighbour Pattern Classification. Journal of Knowledge Based Systems 8(6), 373–389 (1995)

    Article  Google Scholar 

  9. Wittman, T.: Time-Series Clustering and Association Analysis of Financial Data (December 2002), http://www.math.ucla.edu/~wittman/thesis/project.pdf

  10. Bensmail, H., DeGennaro, R.P.: Analyzing Imputed Financial Data: A New Approach to Cluster Analysis (September 2004), http://www.frbatlanta.org/filelegacydocs/wp0420.pdf

  11. Omanovic, S., Avdagic, Z., Konjicija, S.: On-line evolving clustering for financial statements’ anomalies detection. In: International Symposium on Information, Communication and Automation Technologies, ICAT 2009, vol. XXII, pp. 1–4 (2009)

    Google Scholar 

  12. Berzal, F., Cubero, J.-C., Sánchez, D., Serrano, J.: ART: A Hybrid Classification Model. Machine Learning 54(1), 67–92 (2004), doi:10.1023/B:MACH.0000008085.22487.a6

    Article  MATH  Google Scholar 

  13. Min, S.-H., Lee, J., Han, I.: Hybrid genetic algorithms and support vector machines for bankruptcy prediction. Expert Systems with Applications 31(3), 652–660 (2006), doi:10.1016/j.eswa.2005.09.070

    Article  Google Scholar 

  14. Lee, K.C., Han, I., Kwon, Y.: Hybrid neural network models for bankruptcy predictions. Decision Support Systems 18(1), 63–72 (1996), doi:10.1016/0167-9236(96)00018-8

    Article  MathSciNet  Google Scholar 

  15. Quinlan, J.R.: C4.5: programs for machine learning. Morgan Kaufmann Publishers Inc. (1993)

    Google Scholar 

  16. Heckerman, D.: Bayesian networks for knowledge discovery. In: Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R. (eds.) Advances in Knowledge Dicovery and Data Mining, pp. 273–305. MIT Press (1996)

    Google Scholar 

  17. Jahma, W.: A K-Means Clustering Algorithm. Journal of the Royal Statistical Society Series C (Applied Statistics) 28(1), 100–108 (1979)

    Google Scholar 

  18. Shrivastava, V., Khan, M., Chaudhari, V.K.: Neural network learning improvement using K-means clustering algorithm to improve the performance of web traffic mining. Paper Presented at the 2011 3rd International Conference on Electronics Computer Technology (ICECT), Kanyakumari, April 8-10 (2011)

    Google Scholar 

  19. Hagan, M.T., Demuth, H.B., Beale, M.: Neural network design. PWS Publishing Co. (1996)

    Google Scholar 

  20. Rasmussen, C.E.: Gaussian Processes in Machine Learning. In: Bousquet, O., von Luxburg, U., Rätsch, G. (eds.) Machine Learning 2003. LNCS (LNAI), vol. 3176, pp. 63–71. Springer, Heidelberg (2004), doi:10.1007/978-3-540-28650-9_4

    Chapter  Google Scholar 

  21. Carvalho, D.R., Freitas, A.A.: A hybrid decision tree/genetic algorithm method for data mining. Information Sciences 163(1-3), 13–35 (2004), doi:10.1016/j.ins.2003.03.013

    Article  Google Scholar 

  22. Davies, D.L., Bouldin, D.W.: A Cluster Separation Measure. IEEE Trans. Pattern Anal. Mach. Intell. 1(2), 224–227 (1979)

    Article  Google Scholar 

  23. Hofmann, P.D.H.: Statlog (German Credit Data) Data Set. UCI Repository of Machine Learning Databases

    Google Scholar 

  24. Merz CLBaCJ Churn Data Set. UCI Repository of Machine Learning Databases

    Google Scholar 

  25. Housing Market statistics from the Office of National Statistics in the UK, http://www.statistics.gov.uk/hub/people-places/housing-and-households/housing-market

  26. Dhillon, I.S., Modha, D.S.: A Data-Clustering Algorithm on Distributed Memory Multiprocessors. In: Zaki, M.J., Ho, C.-T. (eds.) KDD 1999. LNCS (LNAI), vol. 1759, pp. 245–260. Springer, Heidelberg (2000), doi:10.1007/3-540-46502-2_13

    Chapter  Google Scholar 

  27. Cai, F., Le-Khac, N.-A., Kechai, M.-T.: Clustering Approach for Financial Data analysis. In: The 8th International Conference on Data Mining (DMIN 2012), Nevada, USA, July 16-19 (2012)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Cai, F., LeKhac, NA., Kechadi, MT. (2012). An Integrated Model for Financial Data Mining. In: Sombattheera, C., Loi, N.K., Wankar, R., Quan, T. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2012. Lecture Notes in Computer Science(), vol 7694. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35455-7_28

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-35455-7_28

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-35454-0

  • Online ISBN: 978-3-642-35455-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics