Comparing Machine Learning and Knowledge Discovery in DataBases: An Application to Knowledge Discovery in Texts

This presentation has two goals. The first goal is to compare ML and Knowledge Discovery in Data (KDD, also often called Data Mining, DM) in order to insist 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.