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KDD Pipeline

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Encyclopedia of Database Systems

Synonyms

KDD process; Data mining process; Data mining pipeline

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.

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Recommended Reading

  1. Brachman R. and Anand T. The process of knowledge discovery in databases: a human centered approach. In Proc. 10th National Conf. on AI, 1996, pp. 37–38.

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  2. Fayyad U., Piatetsky-Shapiro G., and Smyth P. From data mining to knowledge discovery in databases. In Proc. 10th National Conf. on AI, 1996, pp. 1–30.

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  3. Fayyad U., Piatetsky-Shapiro G., and Smyth P. Knowledge discovery and data mining: towards a unifying framework. In Proc. 2nd Int. Conf. on Knowledge Discovery and Data Mining, 1996, pp. 82–88.

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© 2009 Springer Science+Business Media, LLC

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Kriegel, HP., Schubert, M. (2009). KDD Pipeline. In: LIU, L., ÖZSU, M.T. (eds) Encyclopedia of Database Systems. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-39940-9_1134

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