Witten, I. H. <br/><br/>
Data mining /  practical machine learning tools and techniques. 

 - 3rd ed. /  Ian H. Witten, Frank Eibe, Mark A. Hall. 
 - Burlington, MA :  Morgan Kaufmann,  c2011. 
 - The Morgan Kaufmann series in data management systems .
<br/><br/>Machine generated contents note: PART I: Machine Learning Tools and Techniques. Ch 1. What's It All About? Ch 2. Input: Concepts, Instances, Attributes. Ch 3. Output: Knowledge Representation. Ch 4. Algorithms: The Basic Methods. Ch 5. Credibility: Evaluating What's Been Learned. PART II: Advanced Data Mining.Ch 6. Implementations: Real Machine Learning Schemes. Ch 7. Data Transformation. Ch 8. Ensemble Learning. Ch 9. Moving On: Applications and Beyond. PART III: The Weka Data MiningWorkbench. Ch 10. Introduction to Weka. Ch 11. The Explorer. Ch 12. The Knowledge Flow Interface. Ch 13. The Experimenter. Ch 14 The Command-Line Interface. Ch 15. Embedded Machine Learning. Ch 16. Writing New Learning Schemes. Ch 17. Tutorial Exercises for the Weka Explorer. 
<br/><br/><label>ISBN: </label>0123748569 (pbk.) 9780123748560 (pbk.) 
<br/><br/><label>LCCN: </label>2010039827 
<br/><br/><label>Subjects--Topical Terms: </label><br/>Data mining.
<br/><br/><label>Index Terms--Genre/Form: </label><br/>Electronic books.