000			01156nam a22002297a 4500
999			_c51190 _d52737
003			ISURa
008			181218b xxu||||| |||| 00| 0 eng d
020			_a9780128042915
041			_aEnglish
082			_a006.312 _b WIT
100			_aWitten, Ian H. _968413
245			_aData mining _bpractical machine learning tools and techniques
250			_a4th ed.
260			_aCambridge, _b MA : Morgan Kaufmann _c2017
300			_axxxii, 621 p. _bsome col. _c23 cm.
500			_a 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
650			_aData mining. _929634
700			_aFrank, Eibe _968414
700			_aHall, Mark A. _968415
700			_aPal, Christopher J. _968416
942			_2ddc _cLN