Databases knowledge discovery

Gaines, B., The trade-off between knowledge and data in knowledge acquisition. In Knowledge Discovery in Databases (G. Shapiro and W. Frawley, eds.), p. 491. MIT Press, Cambridge, MA, 1991. 

Shapiro, G., and Frawley, W eds, Knowledge Discovery in Databases. MIT Press Cambridge, MA. 1991. 

Principles and Practice of Knowledge Discovery in Databases, Lyon, France, 2000. Fast Hierarchical Clustering Based on Compressed Data and OPTICS. 

KDD. Knowledge Discovery in Database application of analysis and data mining techniques to discover "knowledge" that may be implicit but undiscovered in large amounts of data. 

Frawley W, et al. 1992. Knowledge discovery in databases an overview. Al Magazine Fall 213-228. 

P. S. Bradley, U. Fayyad, and C. Reina, Scaling clustering algorithms to large databases, in Fourth International Conference on Knowledge Discovery and Data Mining, AAAI Press, New York, 1998, pp. 9-15. 

Knowledge discovery in databases (KDD) is the main objective in Data Mining. The two terms are often used synonymously, although some authors define Knowledge Discovery as being carried out at a higher level than Data Mining. 

Data Mining embraces a wealth of methods that are used in parts of the overall process of Knowledge Discovery in Databases. The particular Data Mining methods employed need to be matched to the user s requirements for the overall KDD process. 

Research in Data Mining has been led by the KDD (Knowledge Discovery in Databases) annual conferences, several of which have led to books on the subject (e.g., Fayyad etal., 1996). These conferences have grown in 10 years from being a small workshop to a large independent conference with, in Boston in 2000, nearly 1(X)0 participants. The proceedings of these conferences are still the major outlet for new developments in Data Mining. 

It should be noted that discovery of experience from a collection ofknowledge or soeial practice is a significant issue for EM, just as knowledge discovery from a very large database (Sun Finnic, 2005). Further, the processing of experience requires an experience base, where experience processing will be conducted. 

L. Chen and J. Gasteiger, J. Am. Chem. Soc., 118, 4033 (1997). Knowledge Discovery in Reaction Databases Landscaping Organic Reactions by a Self-Organizing Neural Network. 

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