Abstract
Data mining can also be viewed as a process of model building, and thus the data used to build the model can be understood in ways that we may not have previously taken into consideration. This chapter summarizes some well-known data mining techniques and models, such as: Bayesian classifier, association rule mining and rule-based classifier, artificial neural networks, k-nearest neighbors, rough sets, clustering algorithms, and genetic algorithms. Thus, the reader will have a more complete view on the tools that data mining borrowed from different neighboring fields and used in a smart and efficient manner for digging in data for hidden knowledge.
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© 2011 Springer-Verlag Berlin Heidelberg
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Gorunescu, F. (2011). Data Mining Techniques and Models. In: Data Mining. Intelligent Systems Reference Library, vol 12. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19721-5_5
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DOI: https://doi.org/10.1007/978-3-642-19721-5_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-19720-8
Online ISBN: 978-3-642-19721-5
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