Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series in Data Management Systems) 4th Edition by Ian H. Witten (PDF)

Description

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 containsPowerpoint slides for Chapters 1-12. This is a very comprehensive teaching resource, with many PPT slides covering each chapter of the bookOnline Appendix on the Weka workbench; again a very comprehensive learning aid for the open source software that goes with the bookTable of contents, highlighting the many new sections in the 4th edition, along with reviews of the 1st edition, errata, etc.Provides a thorough grounding in machine learning concepts, as well as practical advice on applying the tools and techniques to data mining projectsPresents concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methodsIncludes a downloadable WEKA software toolkit, a comprehensive collection of machine learning algorithms for data mining tasks-in an easy-to-use interactive interfaceIncludes open-access online courses that introduce practical applications of the material in the book

User’s Reviews

Reviews from Amazon users which were colected at the time this book was published on the website:

⭐Very good book for my master’s thesis. Easy to understand and read.

⭐Weka as a beginners learning tool is okay. Its not made for BIG data. If you have the memory you might be able to work with 20k rows but not 30k+. The software isn’t made for it. The GUI interface will start to glitch and you be left with a choppy screen.As for the book its DRY DRY DRY. Like go get a redbull and skittles to pep yourself up from how quickly the material will put you to sleep. Chapters are also longer than they need to be and filled with fluff. If you want to learn read a chapter then watch the authors videos on youtube. Maybe you just need to watch the videos…

⭐I’ve read and reviewed the 1st, 2nd and now the 4th edition. The authors are genuine experts, at the front of their fields, and by adding new contributors have been able to both update existing topics as well as add authoritative treatments of new ones. I recommend this text to anyone seeking a serious introduction to data mining. The emphasis is practical rather than theoretical, but there are pointers to the theoretical literature for those wanting them. The practical emphasis serves those wanting such, and provides motivation and context for the approach. For those with the necessary mathematical, statistical and computing background there are certainly a plethora of more advanced treatments, but Witten et.al. may well be the best available introduction to the subject for almost everyone.

⭐I am using this text in a University (American) Data Mining Certification Program. This book is horrible for learning — truly dreadful attempt by an obviously disinterested professor. It does not help that a worthless SW program is used in the course, Weka, which is hardly recognized within the industry. And for good reason: Weka (termed for some New Zealand bird??) is clunky: the user-interface is poorly designed, the program accepts minimal hyperparameters, and the graphic output is so ugly that you’ll wish for ggplot — or find yourself dumping your output into Excel. The author is a professor at a New Zealand university, and seems friendly enough, although very flighty at time (visit his Youtube channel for instructions on how to use Weka — but don’t expect a thorough review, and be prepared to skip the first 15 seconds of him playing his horn instrument – dreadful). I found an alternative Youtube channel of a Data Science Professor in the US who provided far superior Weka instructions.The Book: it has such poor structure that you’ll want to throw it against a wall and start over: I did. The author, for whatever reason determined to break the book into complexity, vs structuring it by method/model (ideal structure might be: Supervised methods in Section I, Unsupervised methods in Section II). Chapter 3 introduces many DM methods/models (which is appropriate enough), but thereafter he continues with the same structure in the remainder of the text: never creating a section exclusively dedicated to Decision Trees or Regression Models, for example. The result is that it is very difficult to gain in-depth knowledge of any on particular method. You’ll find yourself going back several chapters to re-learn methods, vs building upon your method knowledge one method at a time. Perhaps this is done intentionally as Witten seems to enjoy being cryptic and unhelpful in his Youtube videos. It is clear that he and his co-author have not solicited editing reviews from other ‘good’ authors.The Writing: Imagine a very intelligent Computer Scientist (but very poor – obviously tenured – instructor) recording his own random, unstructured thoughts about a complex topic; to be used by his own University Students. Get it? Witten’s aim in NOT to instruct, but to befuddle and confound his Masters students. Unless you find yourself at the University of …. Waikado (I think is the name), and in his program, you should really not even go here. He constantly utilizes the “e.g.” reference where unnecessary. In several critical places in the text, Witten will title a chapter or section with a DM concept (Clustering, for example) but then never provide a sufficient definition of the concept: it’s bewildering! His co-author can be found online (at CS information sites) providing equally cryptic – and often snotty – answers to peoples’ legitimate inquiries about how to use Weka. The arrogance is certainly not warranted!You will spend so much time defining the concepts on your own – via Google searches – that the book becomes an afterthought in your goal of learning Data Mining.Skip this text — find another if you wish to learn Data Mining.

⭐This is a great textbook for the subject, but this edition has some significant typos in it. The book i received has significant errors in reference to chapters in the book. For example, the opening to part two of the book references the later chapters all incorrectly. The book seems to be legit as far as being genuine so i don’t think i got a knock-off version.

⭐This is one of the best, well written, instructive books on AI/data mining that I’ve ever read. Using their WEKA tool while reading this book is without a doubt an outstanding way to make progress in data mining.

⭐This book seems to have all the content you need to become well informed about the field of data mining. The issue with this book is the authors are so verbose in their writing style. It takes forever to get into the important concepts and demonstrations. I also I’m not a big fan of limited hands-on/walk-through examples within the book using WEKA. Overall this textbook has good content and is useful but very difficult to read through due to the lengthy and unnecessary writing.

⭐This was a gift for someone else. The recipient is enjoying reading and learning from this edition. Thank you for the fast delivery!

⭐Very detailed. Useful for reference. Only starting my journey in data mining but still useful and informative.

⭐One of the best on Data mining.

⭐The book was in great condition. I wish it had a hard cover though.If you want to learn about data mininh (I had to buy this book for an advanced data mining course) I would NOT recommend this book. The content is really of poor quality. Please do NOT use this book in your classes, just waste of money for students.

⭐Lo deje de leer, hay mejores libros con ejemplos prácticos y explicaciones concretas.lo compré porque pensaba que la parte de deep learning estaba bien explicada, pero es similar a lasversiones anteriores, en el sentido de que es demasiado practico, lo único realmente nuevo es el capítulo 9 demetodos probabilisticos.

⭐

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