000			03523nam a22003617a 4500
003			OSt
005			20260723104052.0
008			260723b |||||||| |||| 00| 0 eng d
020			_a9780443158889 [paperback]
040			_aUniversity of Cebu-Banilad _cUniversity of Cebu-Banilad
100			_aWitten, Ian H., _eauthor.
245			_aData mining : _bpractical machine learning tools and techniques / _cby Ian H. Witten, Eibe Frank, Mark A. Hall, Christopher J. Pal, and James R. Foulds.
250			_aFifth edition.
260			_a50 Hampshire Street, 5th floor, Cambridge, MA, 02139 United States : _bElsevier Inc., _cc2026.
300			_axl, 760 pages : _billustrations (black and white) ; _c23 cm.
336			_2rdacontent _atext
337			_2rdamedia _aunmediiated
338			_2rdacarrier _avolume
504			_aIncludes bibliographical references and index.
505			_aContents: 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 -- Chapter 6 Preparation: data preprocessing and exploratory data analysis -- Chapter 7: Ethics: what are the impacts of what's been learned? -- Part II More advanced machine learning schemes -- Chapter 8 Ensemble learning -- Chapter 9 Extending instance-based and linear models -- Chapter 10 Deep learning: fundamentals -- Chapter 11 Advanced deep learning methods -- Chapter 12 Beyond supervised and unsupervised learning -- Chapter 13 Probabilistic methods: fundamentals -- Chapter 14 Advanced probabilistic methods -- Chapter 15 Moving on: applications and their consequences.
520			_a"**2026 Textbook and Academic Authors Association (TAA) Textbook Excellence "Texty" Award Winner**Data Mining: Practical Machine Learning Tools and Techniques, Fifth 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 new 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. Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including more recent deep learning content on topics such as generative AI (GANs, VAEs, diffusion models), large language models (transformers, BERT and GPT models), and adversarial examples, as well as a comprehensive treatment of ethical and responsible artificial intelligence topics. Authors Ian H. Witten, Eibe Frank, Mark A. Hall, and Christopher J. Pal, along with new author James R. Foulds, include today’s techniques coupled with the methods at the leading edge of contemporary research." --Provided by the puublisher
521			_aAdult
541			_aPurchased _xOrtega, Eric _yCollege of Computer Studies _zComputer Science
546			_aText in English
650			_aData mining.
700			_aFrank, Eibe, _eauthor.
700			_aHall, Mark A., _eauthor.
700			_aPal, Christopher J., _eauthor.
700			_aFoulds, James R., _eauthor.
942			_2ddc _cBK
998			_cJanna [new] _d07/23/2026
999			_c15567 _d15567