This is the second edition of the author's Data Mining book. The first part of the book focuses on data mining algorithms, implementation issues, and how to evaluate the results of the data mining model. The second part focuses on the authors "Weka Machine Learning Workbench" which is available under a GNU General Public License. See their web site: http://www.cs.waikato.ac.nz/~ml/weka/index.html for the software. This software appears to be widely used at academic institutions.
The first section of the book provides an overview of the algorithms that the software implements. If you need an in depth understanding of the algorithms, you will need additional information sources. If you simply download the software without an understanding of which algorithms are appropriate to your data mining problem, you may become frustrated with the performance, or, even worse, you may misinterpret the results of the data mining model.
In general, learning data mining is much more complex than this book (or any other single book) can adequately describe; however, this is an excellent source for someone interested in data mining.
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Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Labels: Data Mining