Data Mining: Another Tool To Increase Productivity In Manufacturing?
The definition of Data Mining is one that is often confused. Many feel it is merely a part of the Knowledge Management concept. In fact, Data Mining is referred to in a broader context as Knowledge Discovery in Databases or KDD. It may appear as if Knowledge Management and KDD themselves are recycled concepts. In fact, many definitions of Knowledge Management are very similar to many of its predecessors; Information Systems, Decision Support Systems, Expert Systems and their earlier forms. Not only, do they all exhibit very similar goals, the methods in which they extrapolate information from data are not too dissimilar either.
The Knowledge Management concept "emanates from its earlier definition of capturing, storing and analytically processing that resides in the various companies databases for decision making." Kanter . This does not appear to be any different from that of a Management Information System. Kanter does note that knowledge includes tacit or implicit knowledge of the user which does not exist within any database which does set KM apart from the pack.
Fayyad, et al , however, assert that Knowledge Discovery in Databases and Data Mining are different. "The term knowledge discovery in databases, or KDD for short, was coined in 1989 to refer to the broad process of finding knowledge in data, and to emphasize the 'highlevel' application of particular data mining methods. The term Data Mining has been commonly used by statisticians, data analysts and the MIS (Management Information Systems) community, while Knowledge Discovery in Databases has been mostly used by artificial intelligence and machine learning researchers…Knowledge Discovery in Databases refers to the overall process of discovering useful knowledge from data while data mining refers to the application of algorithms for extracting patterns from data...