Data mining : practical machine learning tools and techniques

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 acclaime...

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Bibliographic Details
Main Authors Witten, I. H. (Author), Frank, Eibe (Author), Hall, Mark A. (Author), Pal, Christopher J. (Author)
Format Electronic eBook
LanguageEnglish
Published Cambridge, MA : Morgan Kaufmann Publisher, [2017]
EditionFourth edition.
Subjects
Data mining.
elektronické knihy
electronic books
Online AccessFull text
ISBN9780128043578
9780128042915
Physical Description1 online zdroj (xxxii, 621 stran) : ilustrace

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TOC from ObalkyKnih.cz
Table of Contents:
  • Part I. Introduction to data mining. 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
  • Part II. More advanced machine learning schemes. 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
  • Chapter 11. Beyond supervised and unsupervised learning
  • Chapter 12. Ensemble learning - Chapter 13. Moving on: applications and beyond
  • Appendix A. Theoretical foundations
  • Appendix B. The WEKA workbench.

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