TY  - BOOK
AU  - Witten,I.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  - Amsterdam, Netherlands
PB  - Morgan Kaufmann Publishers, Elsevier
KW  - Data mining
KW  - NGU-sh
N1  - Includes bibliographical references and index; 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 -- References -- Index
ER  -