Data Mining (inbunden)
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Format
EPUB
Språk
Engelska
Antal sidor
654
Utgivningsdatum
2016-12-20
Upplaga
4
Förlag
Elsevier Science & Technology
Medarbetare
Frank, Eibe (Computer Science Department, University of Waikato, New Zealand) (förf.) / Hall, Mark A. (Computer Science Department, University of Waikato, New Zealand) (förf.) / Pal, Christopher J. (Department of Computer Engineering and Software Engineering, Polytechnique Montreal, Quebec, Canada)
Dimensioner
234 x 188 x 28 mm
Vikt
1317 g
Antal komponenter
1
ISBN
9780128042915

Data Mining

Practical Machine Learning Tools and Techniques

av Ian H Witten, Eibe Frank, Mark A Hall, Christopher J Pal, Ian H Witten
(1 röst)
EPUB,  Engelska, 2016-12-20
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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 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 substantial new chapters on probabilistic methods and on deep learning. Accompanying the book is a new version of the popular WEKA machine learning software from the University of Waikato. Authors Witten, Frank, Hall, and Pal include today's techniques coupled with the methods at the leading edge of contemporary research. Please visit the book companion website at https://www.cs.waikato.ac.nz/~ml/weka/book.html. It contains Powerpoint slides for Chapters 1-12. This is a very comprehensive teaching resource, with many PPT slides covering each chapter of the book Online Appendix on the Weka workbench; again a very comprehensive learning aid for the open source software that goes with the book Table of contents, highlighting the many new sections in the 4th edition, along with reviews of the 1st edition, errata, etc.

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Ian H Witten, Eibe Frank, Mark A Hall, Christopher J Pal, Ian H Witten

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"...this volume is the most accessible introduction to data mining to appear in recent years. It is worthy of a fourth edition." --Computing Reviews



Övrig information

Ian H. Witten is a professor of computer science at the University of Waikato in New Zealand. He directs the New Zealand Digital Library research project. His research interests include information retrieval, machine learning, text compression, and programming by demonstration. He received an MA in Mathematics from Cambridge University, England; an MSc in Computer Science from the University of Calgary, Canada; and a PhD in Electrical Engineering from Essex University, England. He is a fellow of the ACM and of the Royal Society of New Zealand. He has published widely on digital libraries, machine learning, text compression, hypertext, speech synthesis and signal processing, and computer typography. Eibe Frank lives in New Zealand with his Samoan spouse and two lovely boys, but originally hails from Germany, where he received his first degree in computer science from the University of Karlsruhe. He moved to New Zealand to pursue his Ph.D. in machine learning under the supervision of Ian H. Witten and joined the Department of Computer Science at the University of Waikato as a lecturer on completion of his studies. He is now a professor at the same institution. As an early adopter of the Java programming language, he laid the groundwork for the Weka software described in this book. He has contributed a number of publications on machine learning and data mining to the literature and has refereed for many conferences and journals in these areas. Mark A. Hall holds a bachelor's degree in computing and mathematical sciences and a Ph.D. in computer science, both from the University of Waikato. Throughout his time at Waikato, as a student and lecturer in computer science and more recently as a software developer and data mining consultant for Pentaho, an open-source business intelligence software company, Mark has been a core contributor to the Weka software described in this book. He has published several articles on machine learning and data mining and has refereed for conferences and journals in these areas. Christopher J. Pal is a Canada CIFAR AI Chair and a full professor at the Department of Computer Engineering and Software Engineering at Polytechnique Montréal. Pal's research interests include computer vision and pattern recognition, computational photography, natural language processing, statistical machine learning and applications to human computer interaction.

Innehållsförteckning

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

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