KDD Process

See: KDD, Process.

References

(Fayyad et al., 1996) ⇒ Usama M. Fayyad, Gregory Piatetsky-Shapiro, and Padhraic Smyth. (1996). “The KDD Process for Extracting Useful Knowledge from Volumes of Data.” In: Communications of the ACM, 39(11).
- QUOTE: Finding useful patterns in data is known by different names (including data mining) in different communities (e.g., knowledge extraction, information discovery, information harvesting, data archeology, and data pattern processing). The term “data mining” is used most by statisticians, database researchers, and more recently by the MIS and business communities. Here we use the term “KDD” to refer to the overall process of discovering useful knowledge from data. Data mining is a particular step in this process — application of specific algorithms for extracting patterns (models) from data. The additional steps in the KDD process, such as data preparation, data selection, data cleaning, incorporation of appropriate prior knowledge, and proper interpretation of the results of mining ensure that useful knowledge is derived from the data. Blind application of data mining methods can be a dangerous activity leading to discovery of meaningless patterns.
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