Abstract
Data-mining (and machine learning) algorithms try to find interesting valid patterns in data. As such they perform a task similar to a scientist who searches for a theory that explains the data.
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- 1.
Usually epistemology is defined by the question: What can we know? But this question leads to a debate in regard to the definition of knowledge, and unfortunately this tiresome debate seems to lead nowhere. The concept of error is much less problematic than the concept of knowledge, and therefore I prefer to present epistemology by the question: how can we avoid errors when explaining interesting phenomena?
- 2.
The rule was revealed by running the WizWhy data mining program, www.wizsoft.com. This example is also presented in Agassi and Meidan (2016).
References
Agassi, Joseph, and Abraham Meidan. 2008. Philosophy from a skeptical perspective. Cambridge: Cambridge University Press.
———. 2016. Beg to differ: The logic of disputes and argumentation. New York: Springer.
Agrawal, R., et al. 1996. Fast discovery of association rules. In Advances in knowledge discovery and data mining, 307–328. Cambridge, MA: MIT Press.
Fayyad, Usama, Gregory Piatetsky-Shapiro, and Padhraic Smyth. 1996. From data mining to knowledge discovery in databases. http://www.kdnuggets.com/gpspubs/aimag-kdd-overview-1996-Fayyad.pdf
Hume, David. 1740. A treatise of human nature, (1967, edition). Oxford: Oxford University Press.
———. 1777. An enquiry concerning human understanding In: P. N. Nidditch, ed., 3rd ed. (1975), Oxford: Clarendon Press.
Meidan, Abraham, and Levin Boris. 2002. Choosing from competing theories in computerized learning. Mind and Machines 12: 119–129.
Whewell, William. 1922–1958. Alumni cantabrigienses. In Venn, J. and J.A. Cambridge: Cambridge University Press.
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Meidan, A. (2017). Lessons from the Philosophy of Science to Data Mining and Vice Versa. In: Bar-Am, N., Gattei, S. (eds) Encouraging Openness. Boston Studies in the Philosophy and History of Science, vol 325. Springer, Cham. https://doi.org/10.1007/978-3-319-57669-5_10
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