Georges G. Grinstein
Knowledge discovery is the process of discovering interesting, non-trivial patterns in data. In the sub-field called knowledge discovery in databases (KDD) the discovery process targets data repositories, and often includes metrics on the results it has achieved, measuring how good the discoveries are with respect to, for example, non-trivialness, novelty, or extent. Knowledge, the primary goal of data analysis and exploration, is most often discovered by generating information (structure) from data, and then abstracting non-trivial patterns (rules or associations for example) from the information. The discovery process can be done using numerous means that share the same goal: visualization, data mining, statistics, neural networks, or mathematical modeling and simulation.