TY  - BOOK
AU  - Witten,Ian H.
AU  - Frank,Eibe
AU  - Hall,Mark A.
AU  - Pal,Christopher J.
TI  - Data mining: practical machine learning tools and techniques
SN  - 9780128042915
U1  - 006.312 23
PY  - 2017///
CY  - United States
PB  - Morgan Kaufmann
KW  - Data mining
KW  - Procesamiento de datos
KW  - Association rule mining
KW  - Data transformations
N1  - Contents; Chapter 1. What’s it all about?. -- Chapter 2. Input: Concepts, instances, attributes. -- Chapter 3. Output: Knowledge representation. -- Chapter 4. Algorithms: The basic methods. -- Chapter 5. Credibility: Evaluating what’s been learned. -- Chapter 6. Trees and rules. -- Chapter 7. Extending instance-based and linear models. -- Chapter 8. Data transformations. -- Chapter 9. Probabilistic methods. -- Chapter 10. Deep learning. -- 10.5 Stochastic Deep Networks. -- Chapter 11. Beyond supervised and unsupervised learning. -- Chapter 12. Ensemble learning. -- Chapter 13. Moving on: applications and beyond
N2  - Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches
ER  -