02075nam a22003257i 450000500170000000700030001700800410002002000180006104000230007904100080010208200230011010000320013324500680016525000070023326400450024030000410028533600270032633700330035333800280038650000140041450506180042852005490104665000160159565000270161165000280163865000250166670000160169170000180170770000240172520251030135902.0t|230503s2017       ||||| |||| 00| 0 spa d  a9780128042915  aCO-ViULLerdabspa  heng0 223a006.312bW829d1 aWitten, Ian H.eautor.4aut10aData mining :bpractical machine learning tools and techniques.  a4. 1aUnited States :bMorgan Kaufmann,c2017.  a621 páginas :b: figuras , tables.  atextobtxt2rdacontent  asin mediaciónbn2rdamedia  avolumenbnc2rdacarrier  aContents.8 aChapter 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.  aData 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. 0aData mining 0aProcesamiento de datos 0aAssociation rule mining 0aData transformations1 aFrank, Eibe1 aHall, Mark A.1 aPal, Christopher J.