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Knowledge Discovery and Data Mining

The phrase knowledge discovery in databases was first introduced at the first KDD workshop in 1989* to emphasize that knowledge is the end product of a data-driven discovery. Fayyad and al.* first described data mining as a particular step in knowledge discovery process. Upon them, "historically, the notion of finding useful patterns in data has been given a variety of names, including data mining, knowledge extraction, information discovery, information harvesting, data archaeology, and data pattern processing. The term data mining has mostly been used by statisticians, data analysts, and the management information systems (MIS) communities. It has also gained popularity in the database field".

Knowledge discovery process

Knowledge discovery is "the overall process of discovering useful knowledge from data, while data mining refers to a particular step in this process. Data mining is the application of specific algorithms for extracting patterns from data... The additional steps in the KDD process, such as data preparation, data selection, data cleaning, incorporation of appropriate prior knowledge, and proper interpretation of the results of mining, are essential to ensure that useful knowledge is derived from the data. Blind application of data-mining methods can be a dangerous activity, easily leading to the discovery of meaningless and invalid patterns".


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