Data mining
Witten, Ian H.
Data mining practical machine learning tools and techniques - 4th ed. - Cambridge, MA : Morgan Kaufmann 2017 - xxxii, 621 p. some Colour 23 cm.
Part I: Introduction to data mining 1. What's it all about? 2. Input: Concepts, instances, attributes 3. Output: Knowledge representation 4. Algorithms: The basic methods 5. Credibility: Evaluating what's been learned Part II. More advanced machine learning schemes 6. Trees and rules 7. Extending instance-based and linear models 8. Data transformations 9. Probabilistic methods 10. Deep learning 11. Beyond supervised and unsupervised learning 12. Ensemble learning 13. Moving on: applications and beyond
9780128042915
Data mining.
006.312 / WIT
Data mining practical machine learning tools and techniques - 4th ed. - Cambridge, MA : Morgan Kaufmann 2017 - xxxii, 621 p. some Colour 23 cm.
Part I: Introduction to data mining 1. What's it all about? 2. Input: Concepts, instances, attributes 3. Output: Knowledge representation 4. Algorithms: The basic methods 5. Credibility: Evaluating what's been learned Part II. More advanced machine learning schemes 6. Trees and rules 7. Extending instance-based and linear models 8. Data transformations 9. Probabilistic methods 10. Deep learning 11. Beyond supervised and unsupervised learning 12. Ensemble learning 13. Moving on: applications and beyond
9780128042915
Data mining.
006.312 / WIT