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Customer Relationship Management Using Partial Focus Feature Reduction

  • Yan Tu
  • Zijiang Yang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7666)

Abstract

Effective data mining solutions have for long been anticipated in Customer Relationship Management (CRM) to accurately predict customer behavior, but in a lot of research works we have observed sub-optimal CRM classification models due to inferior data quality inherent to CRM data set. This paper is proposed to present our new classification framework, termed Partial Focus Feature Reduction, poised to resolve CRM data set with Reduced Dimensionality using a collection of efficient data preprocessing techniques characterizing a specially tailored modality grouping method to significantly improve feature relevancy as well as reducing the cardinality of the features to reduce computational cost. The resulting model yields very good performance result on a large complicated real-world CRM data set that is much better than ones from complex models developed by renowned data mining practitioners despite all data anomalies.

Keywords

Customer relationship Management Feature reduction Classification Data mining 

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References

  1. 1.
    Kincaid, J.W.: Customer Relationship Management: Getting It Right. Prentice Hall PTR (2003)Google Scholar
  2. 2.
    Ngai, E.W.T., Li, X., Chau, D.C.K.: Application of Data Mining Techniques in Customer Relationship Management: A Literature Review and Classification. Expert Syst. Appl. 36, 2592–2602 (2009)CrossRefGoogle Scholar
  3. 3.
    Zhu, D., Li, X., Wu, S.: Identity Disclosure Protection: A Data Reconstruction Approach for Privacy-preserving Data Mining. Dec. Supp. Syst. 48, 133–140 (2009)CrossRefGoogle Scholar
  4. 4.
    Matatov, N., Rokach, L., Maimon, O.: Privacy-Preserving Data Mining: A Feature Set Partitioning Approach. Inform. Sci. 180, 2696–2720 (2010)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Barry, T.: The Development of the Hierarchy of Effects: An Historical Perspective. Current Issues Research Advert., 251–295 (1987)Google Scholar
  6. 6.
    Guyon, I., Lemaire, V., Boullé, M., Dror, G., Vogel, D.: Analysis of the KDD Cup 2009: Fast Scoring on a Large Orange Customer Database. In: JMLR W&CP, vol. 7, pp. 1–22 (2009)Google Scholar
  7. 7.
    IBM Research: Winning the KDD Cup Orange Challenge with Ensemble Selection. In: W&CP, vol. 7 (2009)Google Scholar
  8. 8.
    Xie, J., Rojkova, V., Pal, S., Coggeshall, S.: A Combination of Boosting and Bagging for KDD Cup 2009 - Fast Scoring on a Large Database. In: JMLR W&CP, vol. 7 (2009)Google Scholar
  9. 9.
    Sorokina, D.: Application of Additive Groves Ensemble with Multiple Counts Feature Evaluation to KDD Cup 2009 Small Data Set. In: JMLR W&CP, vol. 7, pp. 101–109 (2009)Google Scholar
  10. 10.
    Sorokina, D., Caruana, R., Riedewald, M.: Additive Groves of Regression Trees. In: Proceedings of the 18th European Conference on Machine Learning (2007)Google Scholar
  11. 11.
    Doetsch, P., et al.: Logistic Model Trees with AUC Split Criterion for the KDD Cup 2009 Small Challenge. In: JMLR W&CP, vol. 7, pp. 77–88 (2009)Google Scholar
  12. 12.
    Quinlan, J.R.: Induction of Decision Trees. Mach. Learn., 81–106 (1986)Google Scholar
  13. 13.
    Hall, M., et al.: The WEKA Data Mining Software: An Update. SIGKDD Exploration 11 (2009)Google Scholar
  14. 14.
    Fayyad, U.M., Irani, K.B.: Multi-interval Discretization of Continuousvalued Attributes for Classification Learning. In: Proceedings of the International Joint Conference on Uncertainty in AI, pp. 1022–1027 (1993)Google Scholar
  15. 15.
    The KDD Cup 2009 Results (Online) (2009), http://www.kddcuporange.com/results.php?ds=small
  16. 16.
    Polikar, R.: Ensemble Based Systems in Decision Making. IEEE Circuits Syst. Magazine 6, 21–45 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Yan Tu
    • 1
  • Zijiang Yang
    • 1
  1. 1.School of Information TechnologyYork UniversityTorontoCanada

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