Data mining
Machine learning and data mining often employ the same methods and overlap significantly, but while machine learning focuses on prediction, based on "known" properties learned from the training data, data mining focuses on the discovery of (previously) "unknown" properties in the data (this is the analysis step of knowledge discovery in databases). Data mining uses many machine learning methods, but with different goals; on the other hand, machine learning also employs data mining methods as "unsupervised learning" or as a preprocessing step to improve learner accuracy. Much of the confusion between these two research communities (which do often have separate conferences and separate journals, ECML PKDD being a major exception) comes from the basic assumptions they work with: in machine learning, performance is usually evaluated with respect to the ability to "reproduce known" knowledge, while in knowledge discovery and data mining (KDD) the key task is the discovery of previously "unknown" knowledge. Evaluated with respect to known knowledge, an uninformed (unsupervised) method will easily be outperformed by other supervised methods, while in a typical KDD task, supervised methods cannot be used due to the unavailability of training data.