Data Mining and Knowledge Discovery in Databases

Data Mining and Knowledge Discovery in Databases

Ana Azevedo (Polytechnic Institute of Porto, ISCAP, Portugal & Algoritmi R&D Center, University of Minho, Portugal)
Copyright: © 2015 |Pages: 10
DOI: 10.4018/978-1-4666-5888-2.ch164
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Data Mining And The Knowledge Discovery In Databases Process

“The KDD process, as presented in (Fayyad, Piatetski-Shapiro, & Smyth, 1996), is the process of using DM methods to extract what is considered knowledge according to the specification of measures and thresholds, using a database along with any required preprocessing, sub sampling, and transformation of the database. There are five stages considered, namely, selection, preprocessing, transformation, data mining, and interpretation/evaluation as presented in Figure 1:

Figure 1.

The KDD process

  • Selection: This stage consists on creating a target data set, or on focusing in a subset of variables or data samples, on which discovery is to be performed;

  • Preprocessing: This stage consists on the target data cleaning and preprocessing in order to obtain consistent data;

  • Transformation: This stage consists on the transformation of the data using dimensionality reduction or transformation methods;

  • Data Mining: This stage consists on the searching for patterns of interest in a particular representational form, depending on the DM objective (usually, prediction);

  • Interpretation/Evaluation: This stage consists on the interpretation and evaluation of the mined patterns.” (Azevedo & Santos, 2008, p. 183)

Key Terms in this Chapter

Data Mining: One of the phases of the KDD process and concerns, mainly, to the means by which the patterns/models are extracted and enumerated from data. Many times is identified with the complete KDD process.

Data Mining Language: Allows users to directly manipulate data and models at the same level.

Data Mining Models: Are obtained from the application of data mining methods/algorithms. There simple models, such as rules or trees, and more complex models, such as neural networks.

Data Mining Algorithms: A sequence of steps that allows obtaining a data mining model.

Data Mining Task: What is pretended to achieve when applying Data Mining, for instance classify.

Standard: That obeys to settled parameters.

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