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IBM Journal of Research and Development
Business Optimization   Volume 51, Number 3/4, 2007
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Statistical methods for automated generation of service engagement staffing plans - References

 by J. Hu,
B. K. Ray,
and M. Singh
References

  1. T. Deong and A. Nanda, Professional Services Text and Cases, McGraw-Hill, Boston, MA, 2003.
  2. R. Melik, L. Melik, A. Bitton, G. Gerdebes, and A. Israilian, Professional Services Automation, Optimizing Project and Service Oriented Organizations, John Wiley and Sons, New York, 2002.
  3. D. A. Aranda, “Service Operations Strategy, Flexibility and Performance in Engineering Consulting Firms,” Intl. J. Oper. & Production Manage. 23, No. 11, 1401–1421 (2003).
  4. P. Coombs, IT Project Estimation: A Practical Guide to the Costing of Software, Cambridge University Press, New York, 2003.
  5. J. MacQueen, “Some Methods for Classification and Analysis of Multivariate Observations,” Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, University of California Press, Berkeley, 1967, pp. 281–297.
  6. A. K. Jain and R. C. Dubes, Algorithms for Clustering Data, Prentice-Hall, Upper Saddle River, NJ, 1988.
  7. S. Z. Selim and M. A. Ismail, “K-Means-Type Algorithms: A Generalized Convergence Theorem and Characterization of Local Optimality,” IEEE Trans. Pattern Anal. & Machine Intell. 6, 81–87 (1984).
  8. P. Bradley and U. Fayyad, “Refining Initial Points for k-Means Clustering,” Proceedings of the 15th International Conference on Machine Learning, Madison, WI, 1998, pp. 91–99.
  9. D. E. Brown and C. L. Huntley, “A Practical Application of Simulated Annealing to Clustering,” Technical Report IPC-91-03, University of Virginia, Charlottesville, 1990; see http://portal.acm.org/citation.cfm?coll=GUIDE&dl=GUIDE&id=900571.
  10. D. Pelleg and A. Moore, “X-Means: Extending K-Means with Efficient Estimation of the Number of Clusters,” Proceedings of the 17th International Conference on Machine Learning, San Francisco, CA, 2000, pp. 727–734.
  11. T. Zhang, R. Ramakrishnan, and M. Livny, “BIRCH: An Efficient Data Clustering Method for Very Large Databases,” Proceedings of the 1998 ACM–SIGMOD International Conference on Management of Data, Seattle, WA, June 1998, pp. 73–84.
  12. G. Karypis, E.-H. Han, and V. Kumar, “Multilevel Refinement for Hierarchical Clustering,” Technical Report 99-020, Department of Computer Science, University of Minnesota, Minneapolis, 1999; see http://glaros.dtc.umn.edu/gkhome/node/153.
  13. B. Chen, P. C. Tai, R. Harrison, and Y. Pan, “Novel Hybrid Hierarchical-K-Means Clustering Method (H-K-Means) for Microarray Analysis,” Proceedings of the 2005 IEEE Computational Systems Bioinformatics Conference Workshops (BCSBW'05), Stanford, CA, 2005.
  14. M. J. Greenacre, Theory and Application of Correspondence Analysis, Academic Press, London, 1984.
  15. L. Kaufman and R. Rousseeuw, Finding Groups in Data: An Introduction to Cluster Analysis, John Wiley & Sons, New York, 1990.
  16. R. Ng and J. Han, “Efficient and Effective Clustering Methods for Spatial Data Mining,” Proceedings of the 20th International Conference on VLDB, Santiago, Chile, 1994, pp. 144–145.
  17. C. Fraley and A. Raftery, “How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis,” The Computer J. 14, No. 8, 578–588 (1998).


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