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Definition
The KDD pipeline describes the complete process of knowledge discovery in databases (KDD), i.e. the process of deriving useful, valid and non-trivial patterns from a large amount of data. The pipeline consists of five consecutive steps:
Selection
The selection step identifies the goal of the current application and selects a data set that is likely to contain relevant patterns.
Preprocessing
The preprocessing step increases the quality of the data set by supplementing missing attributes, removing duplicate instances and resolving data inconsistencies.
Transformation
The transformation step deletes correlated and irrelevant attributes and derives new more meaningful attributes from the current data description.
Data Mining
This step selects a data mining algorithm with respect to the goal which was identified in the selection step and derives patterns or learns functions that are valid for the current data set.
Ev...
Recommended Reading
Brachman R, Anand T. The process of knowledge discovery in databases: a human centered approach. Proceedings of 10th National Conference on AI; 1996. p. 37–8.
Fayyad U, Piatetsky-Shapiro G, Smyth P. From data mining to knowledge discovery in databases. Proceedings of 10th National Conference on AI; 1996. p. 1–30.
Fayyad U, Piatetsky-Shapiro G, Smyth P. Knowledge discovery and data mining: towards a unifying framework. Proceedings of 2nd Internatinal Conference on Knowledge Discovery and Data Mining; 1996. p. 82–8.
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Kriegel, HP., Schubert, M. (2017). KDD Pipeline. In: Liu, L., Özsu, M. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4899-7993-3_1134-2
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DOI: https://doi.org/10.1007/978-1-4899-7993-3_1134-2
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