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Analytical Techniques Help Enhance the Results of Data Mining: Case Study of Cow Insemination

  • Griselda AcostaEmail author
  • Eric Smith
  • Vladik Kreinovich
Chapter
  • 13 Downloads
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 286)

Abstract

Once we have the information about the system, information coming from measurements and from expert estimates, we use this information to come up with a model describing the system. The usual way to come up with such a model is to formulate several different hypotheses and to select the one that best fits the data. Techniques for formulating hypotheses based on the available information are known as data mining techniques. When the amount of data is not sufficient to make statistically justified conclusions, the dependencies produced by data mining techniques are often caused by accidental coincidences and do not reflect the actual behavior of the corresponding system.

References

  1. 1.
    Acosta, G., Smith, E., Kreinovich, V.: Unexpected empirical dependence of calf gender on insemination time: system-based explanation. Appl. Math. Sci. 13(14), 681–684 (2019)Google Scholar
  2. 2.
    Friedman, T.L.: Thank You For Being Late: An Optimists’ Guide to Thriving in the Age of Accelerations. Picador, New York (2016)Google Scholar

Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Griselda Acosta
    • 1
    Email author
  • Eric Smith
    • 2
  • Vladik Kreinovich
    • 3
  1. 1.University of Texas at El PasoEl PasoUSA
  2. 2.Department of Industrial, Manufacturing, and Systems EngineeringUniversity of Texas at El PasoEl PasoUSA
  3. 3.Department of Computer ScienceUniversity of Texas at El PasoEl PasoUSA

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