Abstract
Data Mining, as defined in 1996 by Piatetsky-Shapiro ([1]) is a step (crucial, but a step nevertheless) in a KDD (Knowledge Discovery in Data Bases) process. The Piatetsky-Shapiro’s definition states that the KDD process consists of the following steps: developing an understanding of the application domain, creating a target data set, choosing the data mining task i.e. deciding whether the goal of the KDD process is classification, regression, clustering, etc..., choosing the data mining algorithm(s), data preprocessing, data mining (DM), interpreting mined patterns, deciding if a re-iteration is needed, and consolidating discovered knowledge.
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Wasilewska, A., Menasalvas, E., Scharff, C. (2007). A Model PM for Preprocessing and Data Mining Proper Process. In: Peters, J.F., Skowron, A., Düntsch, I., Grzymała-Busse, J., Orłowska, E., Polkowski, L. (eds) Transactions on Rough Sets VI. Lecture Notes in Computer Science, vol 4374. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71200-8_21
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DOI: https://doi.org/10.1007/978-3-540-71200-8_21
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