Knowledge Discovery in Databases (KDD)

From InfoVis:Wiki
Revision as of 10:01, 9 October 2007 by Iwolf (talk | contribs)
Jump to navigation Jump to search
KDD refers to the overall process of discovering useful knowledge from data. [De Martino et al., 2002]
KDD is an integration of multiple technologies for data management such as database management and data warehousing, statistic machine learning, decision support, and others such as visualisation and parallel computing.
[De Martino et al., 2002]

KDD refers to the overall process of discovering useful knowledge from data, and data mining refers to a particular step in this process.
The basic problem addressed by the KDD process is one of mapping low-level data into other forms that might be more compact, more abstract, or more useful.
KDD focuses on the overall process of knowledge discovery from data, including how the data are stored and accessed, how algorithms can be scaled to massive data sets ultimate and still run efficiently, how results can be interpreted and visualized, and how the overall man-machine interaction can usefully be modeled and supported.
[Fayyad et al., 1996]


  • [De Martino et al., 2002] M. De Martino, A. Bertone, R. Albertoni, H. Hauska, U. Demsar, M. Dunkars. Technical Report of Data Mining, INVISIP IST-2000-29640, Information Visualisation for Site Planning, WP No2: Technology Analysis, D2.2, 28.2.2002
  • [Fayyad et al., 1996] U. Fayyad, G. P.-Shapiro, and P. Smyth. From data mining to knowledge discovery in databases. AI Magazine, 17(3):37-54, Fall 1996.