Skip to main content
Have a personal or library account? Click to login
Number of Clusters and the Quality of Hybrid Predictive Models in Analytical CRM Cover

Number of Clusters and the Quality of Hybrid Predictive Models in Analytical CRM

Open Access
|Aug 2014

References

  1. Blake, C.L., Merz, C.J. (1998) Churn Data Set, UCI Repository of Machine Learning Databases. http://www.sgi.com/tech/mlc/db, University of California, Department of Information and Computer Science, Irvine, CA.
  2. Blattberg, R.C., Kim, B-D, Neslin, S.A., (2008), New York: Springer.
  3. Bose, I., Chen, X. (2009). Hybrid Models Using Unsupervised Clustering for Prediction of Customer Churn.. vol. 19, no. 2, April-June, 133–151.
  4. Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J. (1984).. Belmont, CA: Wadsworth International Group.
  5. Caliński, R.B, Harabasz, J. (1974). A Dendrite Method for Cluster Analysis.. vol. 3, iss. 1, 1–27.
  6. Causality Workbench. Challenges in Machine Learning, http://www.causality.inf.ethz.ch/data/CINA.html.
  7. Christopher, M., Payne, A., Ballantyne, D. (2002).. Oxford: Elsevier.
  8. Chu, B-H., Tsai, M-S., Ho, Ch-S. (2007). Toward a Hybrid Data Mining Model for Customer Retention.. no. 20, 703–718.
  9. Davies, D.L., Bouldin, D.W. (1979). A Cluster Separation Measure. In, vol. 1, no. 2, 224–227.
  10. Everit, B.S., Landau, S., Leese, M., Stahl, D. (2011).. Chichester: John Wiley & Sons.
  11. Ferraretti, D., Lamma, E., Gamberoni, G., Febo, M., Di Cuia, R. (2011). Integrating Clustering and Classification Techniques: A Case Study for Reservoir Facies Prediction. In D. Ryzko et al., SCI 369, Berlin Heidelberg: Springer-Verlag, 21–34.
  12. Frank, A., Asuncion, A. (2010). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.
  13. Gaddam, S.R., Phoha, V.V., Balagani, K.S. (2007). K-means + ID3: A Novel Method for Supervised Anomaly Detection by Cascading K-means Clustering and ID3 Decision Tree Learning Methods. In:, vol. 19, no. 3, March, 345–354.
  14. Hartigan, J.A. (1975).. New York, London, Sydney, Toronto: Wiley.
  15. Hartigan, J.A., Wong, M.A. (1979). A K-means Clustering Algorithm.. vol. 28, no. 1, 100–108.
  16. KDD Cup 2009, http://www.kddcup-orange.com.
  17. Khan, D.M., Mohamudally, N. (2011). An Integration of K-means and Decision Tree (ID3) Towards a More Efficient Data Mining Algorithm.. vol. 3, iss. 12, December, 76–82.
  18. Krzanowski, W.J., Lai, Y.T. (1988). A Criterion for Determining the Number of Groups in a Data Set Using Sum-of-Squares Clustering.. vol. 44, no. 1, 23–34.
  19. Kumar, V., Rathee, N. (2011). Knowledge Discovery from Database Using an Integration of Clustering and Classification.. vol. 2, no. 3, March, 29–33.
  20. Łapczyński, M., Jefmański, B. (2013). Impact of Cluster Validity Measures on Performance of Hybrid Models Based on K-means and Decision Trees. In P. Perner (Ed.),. Ibai Publishing, 153–162.
  21. Łapczyński, M., Surma, J. (2012). Hybrid Predictive Models for Optimizing Marketing Banner Ad Campaign in On-line Social Network. In R. Stahlbock, G.M. Weiss (Eds.), Las Vegas Nevada, USA: CSREA Press, 140–146.
  22. Li, Y., Deng, Z., Qian, Q., Xu, R. (2011). Churn Forecast Based on Two-step Classification in Security Industry.. no. 3, 160–165.
  23. Moro, S., Laureano, R., Cortez, P. (2011). Using Data Mining for Bank Direct Marketing: An Application of the CRISP-DM Methodology. In P. Novais et al. (Eds.), Guimarães, Portugal, October, 117–121.
  24. Shouman, M., Turner, T., Stocker, R. (2012). Integrating Decision Tree and K-Means Clustering with Different Initial Centroid Selection Methods in the Diagnosis of Heart Disease Patients. In R. Stahlbock, G.M. Weiss (Eds.), Las Vegas Nevada, USA: CSREA Press, 24–30.
  25. Tibshirani, R., Walther, G., Hastie, T. (2001). Estimating the Number of Clusters in a Data Set via the Gap Statistic.. ser. B, 63, part 2, 411–423.
  26. van der Putten, P., van Someren, M. (Eds) (2000). CoIL Challenge 2000: The Insurance Company Case. In Also a Leiden Institute of Advanced Computer Science Technical Report 2000–09, Sentient Machine Research, Amsterdam, June 22.
  27. Wierenga, B. (Ed.) (2008).. New York: Springer.
DOI: https://doi.org/10.2478/slgr-2014-0022 | Journal eISSN: 2199-6059 (formerly 0860-150X) | Journal ISSN: 0860-150X
Language: English
Page range: 141 - 157
Published on: Aug 8, 2014
Published by: University of Białystok
In partnership with: Paradigm Publishing Services
Related subjects:

© 2014 Mariusz Łapczyński, Bartłomiej Jefmański, published by University of Białystok
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.