Machine Learning, Knowledge Discovery in Databases (KDD) and Data Mining
Two terms are commonly confused, as they often employ the same methods and overlap significantly. They can be roughly defined as follows:
- Machine learning focuses on prediction, based on known properties learned from the training data.
- Data mining (which is the analysis step of Knowledge Discovery in Databases) focuses on the discovery of (previously) unknown properties on the data.
The two areas overlap in many ways: data mining uses many machine learning methods, but often with a slightly different goal in mind. 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 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 supervised methods, while in a typical KDD task, supervised methods cannot be used due to the unavailability of training data.
Read more about this topic: Machine Learning
Famous quotes containing the words machine, knowledge, discovery, data and/or mining:
“The machine invades me all day.”
—Sharon Atkins, U.S. receptionist. As quoted in Working, book 2, by Studs Terkel (1973)
“The endless cycle of idea and action,
Endless invention, endless experiment,
Brings knowledge of motion, but not of stillness;
Knowledge of speech, but not of silence;
Knowledge of words, and ignorance of the Word.
All our knowledge brings us nearer to our ignorance.”
—T.S. (Thomas Stearns)
“He is not a true man of science who does not bring some sympathy to his studies, and expect to learn something by behavior as well as by application. It is childish to rest in the discovery of mere coincidences, or of partial and extraneous laws. The study of geometry is a petty and idle exercise of the mind, if it is applied to no larger system than the starry one.”
—Henry David Thoreau (1817–1862)
“To write it, it took three months; to conceive it three minutes; to collect the data in it—all my life.”
—F. Scott Fitzgerald (1896–1940)
“For every nineteenth-century middle-class family that protected its wife and child within the family circle, there was an Irish or a German girl scrubbing floors in that home, a Welsh boy mining coal to keep the home-baked goodies warm, a black girl doing the family laundry, a black mother and child picking cotton to be made into clothes for the family, and a Jewish or an Italian daughter in a sweatshop making “ladies” dresses or artificial flowers for the family to purchase.”
—Stephanie Coontz (20th century)