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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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)
Friedman, T.L.: Thank You For Being Late: An Optimists’ Guide to Thriving in the Age of Accelerations. Picador, New York (2016)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2020 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Acosta, G., Smith, E., Kreinovich, V. (2020). Analytical Techniques Help Enhance the Results of Data Mining: Case Study of Cow Insemination. In: Towards Analytical Techniques for Systems Engineering Applications. Studies in Systems, Decision and Control, vol 286. Springer, Cham. https://doi.org/10.1007/978-3-030-46413-4_7
Download citation
DOI: https://doi.org/10.1007/978-3-030-46413-4_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-46412-7
Online ISBN: 978-3-030-46413-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)