Abstract
In this Chapter we discuss a number of parallel data mining algorithms, grouped according to knowledge discovery paradigms (Chapter 2). Hence, parallel versions of rule induction, instance-based learning, genetic algorithms and neural networks algorithms are discussed in turn. However, for the reasons mentioned in Section 2.6, parallel rule induction will be treated in somewhat more detail than the other paradigms. A major theme of this Chapter is an analysis of the differences between control and data parallelism (Chapter 7) in the context of each of the previously-mentioned knowledge discovery paradigms. We also discuss, at the end of the Chapter, the topic of hybrid data/control parallelism.
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© 2000 Springer Science+Business Media New York
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Freitas, A.A., Lavington, S.H. (2000). Parallel Data Mining without DBMS Facilities. In: Mining Very Large Databases with Parallel Processing. The Kluwer International Series on Advances in Database Systems, vol 9. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-5521-6_11
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DOI: https://doi.org/10.1007/978-1-4615-5521-6_11
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4613-7523-4
Online ISBN: 978-1-4615-5521-6
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