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A Novel Method for Extension Transformation Knowledge Discovering

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNISA,volume 7234)

Abstract

On the foundation of analyzing the existing classification, an acquisition method of extension transformation knowledge based on Decision Tree classification has been proposed The new-bored method re-mines and transforms the decision tree rules to "can’t to can, not to yes" strategy which aims to provide targeted decision-making on the transformation of the customer churn by flexible use of the extension set and extension transformation theory. Its practice in a web company has proved that this method is highly feasible, and also has the reference value for other methods research based on Extenics.

Keywords

  • Extension transformation
  • transformation knowledge
  • decision tree
  • Rules Mining customer retention

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References

  1. Han, J., Micheline, K.: Data Mining: Concepts and Techniques, 2nd edn. Morgan Kaufmann (2006)

    Google Scholar 

  2. Olson, D., Shi, Y.: Introduction to business data mining, International edition. McGraw-Hill (2007)

    Google Scholar 

  3. Nie, G.-L., Zhang, L.-L., Li, X.-S., Shi, Y.: The Analysis on the Customers Churn of Charge Email based on Data mining. In: Sixth IEEE International Conference on Data Mining - Workshops (ICDMW 2006), Hong Kong, China, pp. 843–847 (December 2006)

    Google Scholar 

  4. Wen, C.: The Matter-element Model and Its application, 1st edn. Science and Technology Document Publishing House, Beijing (1994) (in Chinese)

    Google Scholar 

  5. Lixi, L., Huawen, L., Chunyan, Y.: Study on the Application of Extenics in Data Mining. Engineering Science 6(7), 53–79 (2004)

    Google Scholar 

  6. Zhang, Y.-L., He, B.: Potential Information Mining Based on Matter-element Extensibility. Mathematics in Practice and Theory 31(5), 569–575 (2001)

    MathSciNet  Google Scholar 

  7. Li, X.-S., Shi, Y., Li, A.-H.: Study on enterprise data mining solution based on extension set. Journal of Harbin Institute of Technology 38(7), 1124–1128 (2006)

    Google Scholar 

  8. Li, L., Yang, C., Li, H.: Extension Strategy Generating System. Science Press, Beijing (2006) (in Chinese)

    Google Scholar 

  9. Li, L., Yang, C., Li, H.: Extension Strategy Generating System. Science Press, Beijing (2006) (in Chinese)

    Google Scholar 

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© 2012 Springer-Verlag Berlin Heidelberg

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Li, X., Xiang, Z., Zhang, H., Zhu, Z. (2012). A Novel Method for Extension Transformation Knowledge Discovering. In: Wang, H., et al. Web Technologies and Applications. APWeb 2012. Lecture Notes in Computer Science, vol 7234. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29426-6_7

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  • DOI: https://doi.org/10.1007/978-3-642-29426-6_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29425-9

  • Online ISBN: 978-3-642-29426-6

  • eBook Packages: Computer ScienceComputer Science (R0)