Abstract
This chapter has two goals. The first goal is to compare Machine Learning (ML) and Knowledge Discovery in Data (KDD, also often called Data Mining, DM) insisting on how much they actually differ. In order to make my ideas somewhat easier to understand, and as an illustration, I will include a description of several research topics that I find relevant to KDD and to KDD only. The second goal is to show that the definition I give of KDD can be almost directly applied to text analysis, and that will lead us to a very restrictive definition of Knowledge Discovery in Texts (KDT). I will provide a compelling example of a real-life set of rules obtained by what I call KDT techniques.
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Kodratoff, Y. (2001). Comparing Machine Learning and Knowledge Discovery in DataBases: An Application to Knowledge Discovery in Texts. In: Paliouras, G., Karkaletsis, V., Spyropoulos, C.D. (eds) Machine Learning and Its Applications. ACAI 1999. Lecture Notes in Computer Science(), vol 2049. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44673-7_1
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DOI: https://doi.org/10.1007/3-540-44673-7_1
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