Abstract
Various models for the KDD (Knowledge Discovery in Databases) process are known, which mainly differ with respect to the number and description of process activities. We present a process unification by assigning the single steps of these models to five main stages and concentrate on data mining aspects. An overview concerning data mining software tools with focus on inbuilt algorithms and additional support provided for the main stages of the KDD process is given within a classification and positioning framework. Finally, an application of a modification of an association rule algorithm is used as empirical example to demonstrate what can be expected when data mining tools are used to handle large data sets.
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© 1999 Springer-Verlag Berlin · Heidelberg
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Gaul, W., Säuberlich, F. (1999). Classification and Positioning of Data Mining Tools. In: Gaul, W., Locarek-Junge, H. (eds) Classification in the Information Age. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-60187-3_13
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DOI: https://doi.org/10.1007/978-3-642-60187-3_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-65855-9
Online ISBN: 978-3-642-60187-3
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