Data Mining: A Lifetime Passion

Chapter

Abstract

I loved watching and playing sports as a boy, and I began playing Farm League at age 8 and then Little League from age 10 to 12. I loved pitching because of the cognitive battle: nothing was more fun than figuring out a way to make the hitter look foolish by throwing a pitch that was unexpected. Over time, I began to notice patterns in individual hitters’ approaches that could be exploited. My father and I would spend hours discussing hitters and how to get them out, including which pitch sequences would work best for each hitter. I did not think about it at that time, but in retrospect, this was data collection and predictive modeling in a rudimentary (but exciting!) form. The only thing I was missing was storing the transactional data in an Oracle database.

References

  1. 1.
    R. Hecht-Nielsen, Neurocomputing (Addison-Wesley, Reading, MA, 1990)Google Scholar
  2. 2.
    R.L. Barron, D.W. Abbott, et al. Trajectory Optimization and Optimum Path-to-Go Guidance of Tactical Weapons: Vol. I—Theory and AIWS Application, August, 1988; Vol. II—Closed-Loop OPTG Guidance of Mk 82 Glide Weapon, September 1987; Vol. III—Open-Loop Trajectory Optimization of Skipper Boost-Glide Weapon, June 1988; Vol. IV—Calculation of Lagrange Multipliers of Vertical-Plane Maximum-Range Trajectories of 11:1 LID Boost-Glide AIWS, January 1989, Barron Associates, Inc. Final Technical Report for HR Textron Inc. under U.S. Naval Weapons Center contract N60530-88-C-0036Google Scholar
  3. 3.
    R.L. Barron, D.W. Abbott, Use of polynomial networks in optimum real-time, two-point boundary-value guidance of tactical weapons, in Proceedings of the Military Computing Conference, 3–5 May 1988Google Scholar
  4. 4.
    M. Young, D. Argiro, S. Kubica, Cantata: visual programming environment for the khoros system. Computer Graphics 29(2), 22–24 (1995)CrossRefGoogle Scholar
  5. 5.
    J.F. Elder, D.W. Abbott, A Comparison of Leading Data Mining Tools, in 4th International Conference on Knowledge Discovery and Data Mining (KDD-98), New York, NY, August 1998Google Scholar
  6. 6.
    D.W. Abbott, I.P. Matkovsky, J.F. Elder, An evaluation of high-end data mining tools for fraud detection, in 1998 IEEE International Conference on Systems, Man, and Cybernetics, San Diego, CA, October 1998Google Scholar
  7. 7.
    D.W. Abbott, H. Vafaie, M. Hutchins, D. Riney, Improper Payment Detection in Department of Defense Financial Transactions (320KB), Federal Data Mining Symposium, Washington, DC, March 28–29, 2000. http://www.abbott-consulting.com/pubs/afcea_2000.pdf
  8. 8.
    D.W. Abbott, Model Ensembles in Clementine, SPSS 2000 Public Sector Users’ Exchange, Washington, DC, December 6, 2000Google Scholar
  9. 9.
    D.W. Abbott and D. Riney, Predictive Modeling to Detect Fraud at DFAS, Predictive Analytics World-Government, Washington, DC, September 12, 2011Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Abbott Analytics, Inc.San DiegoUSA

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