| 000 -LEADER |
|---|
| fixed length control field |
03523nam a22003617a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20260723104052.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
260723b |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
9780443158889 [paperback] |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
University of Cebu-Banilad |
| Transcribing agency |
University of Cebu-Banilad |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Witten, Ian H., |
| Relator term |
author. |
| 245 ## - TITLE STATEMENT |
|---|
| Title |
Data mining : |
| Remainder of title |
practical machine learning tools and techniques / |
| Statement of responsibility, etc |
by Ian H. Witten, Eibe Frank, Mark A. Hall, Christopher J. Pal, and James R. Foulds. |
| 250 ## - EDITION STATEMENT |
|---|
| Edition statement |
Fifth edition. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Place of publication, distribution, etc |
50 Hampshire Street, 5th floor, Cambridge, MA, 02139 United States : |
| Name of publisher, distributor, etc |
Elsevier Inc., |
| Date of publication, distribution, etc |
c2026. |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xl, 760 pages : |
| Other physical details |
illustrations (black and white) ; |
| Dimensions |
23 cm. |
| 336 ## - CONTENT TYPE |
|---|
| Source |
rdacontent |
| Content type term |
text |
| 337 ## - MEDIA TYPE |
|---|
| Source |
rdamedia |
| Media type term |
unmediiated |
| 338 ## - CARRIER TYPE |
|---|
| Source |
rdacarrier |
| Carrier type |
volume |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc |
Includes bibliographical references and index. |
| 505 ## - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
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 -- 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 ## - SUMMARY, ETC. |
|---|
| Summary, etc |
"**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.<br/><br/>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.<br/><br/>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 ## - TARGET AUDIENCE NOTE |
|---|
| Target audience note |
Adult |
| 541 ## - IMMEDIATE SOURCE OF ACQUISITION NOTE |
|---|
| Source of acquisition |
Purchased |
| Deans/Chairperson |
Ortega, Eric |
| Department |
College of Computer Studies |
| Subject Category |
Computer Science |
| 546 ## - LANGUAGE NOTE |
|---|
| Language note |
Text in English |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Data mining. |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Frank, Eibe, |
| Relator term |
author. |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Hall, Mark A., |
| Relator term |
author. |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Pal, Christopher J., |
| Relator term |
author. |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Foulds, James R., |
| Relator term |
author. |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
|
| Type of record |
Book |
| 998 ## - LOCAL CONTROL INFORMATION (RLIN) |
|---|
| Encoded by |
Janna [new] |
| Date encoded |
07/23/2026 |
