Abstract
This chapter gives an account of the nine Laws of Data Mining, and proposes two hypotheses about data mining and cognition. The nine Laws describe key properties of the data mining process, and their explanations explore the reasons behind these properties. The first hypothesis is that data mining is a kind of intelligence amplifier, because the data mining process enables the data miner to see things which they could not see unaided, as stated in the sixth law of data mining. The second hypothesis is that machine learning algorithms have a special value to data mining because they represent knowledge in a way which is cognitively plausible, and this makes them more suitable for intelligence amplification.
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Acknowledgments
I would like to thank Chris Thornton and David Watkins, who inspired the initial concepts behind this work, Chris Thornton again for his help in formulating NFL-DM, and also all those who have contributed to the LinkedIn discussion group ‘9 Laws of Data Mining’, which has provided invaluable food for thought.
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Khabaza , T. (2014). From Cognitive Science to Data Mining: The First Intelligence Amplifier. In: Wyatt, J., Petters, D., Hogg, D. (eds) From Animals to Robots and Back: Reflections on Hard Problems in the Study of Cognition. Cognitive Systems Monographs, vol 22. Springer, Cham. https://doi.org/10.1007/978-3-319-06614-1_13
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DOI: https://doi.org/10.1007/978-3-319-06614-1_13
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