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Data Mining: Practical Machine Learning Tools and Techniques, 4th Edition
Data Mining: Practical Machine Learning Tools and Techniques, 4th EditionНазвание: Data Mining: Practical Machine Learning Tools and Techniques, 4th Edition
Автор: Ian H. Witten, Eibe Frank, Mark A. Hall
Издательство: Morgan Kaufmann
Год: 2017
Страниц: 655
Язык: английский
Формат: pdf (true)
Размер: 12.6 MB

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 (DL).

Data mining is the extraction of implicit, previously unknown, and potentially useful information from data. The idea is to build computer programs that sift through databases automatically, seeking regularities or patterns. Strong patterns, if found, will likely generalize to make accurate predictions on future data. Of course, there will be problems. Many patterns will be banal and uninteresting. Others will be spurious, contingent on accidental coincidences in the particular dataset used. And real data is imperfect: some parts will be garbled, some missing. Anything that is discovered will be inexact: there will be exceptions to every rule and cases not covered by any rule. Algorithms need to be robust enough to cope with imperfect data and to extract regularities that are inexact but useful.

Machine Learning (ML) provides the technical basis of data mining. It is used to extract information from the raw data in databases—information i.e., ideally, expressed in a comprehensible form and can be used for a variety of purposes. The process is one of abstraction: taking the data, warts and all, and inferring whatever structure underlies it. This book is about the tools and techniques of Machine Learning that are used in practical data mining for finding, and if possible describing, structural patterns in data.

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. 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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