Data Mining: Practical Machine Learning Tools and Techniques (3rd Edition)

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He has written several books, the latest being Managing Gigabytes and Data Mining , both from Morgan Kaufmann.

Data Mining: Practical Machine Learning Tools and Techniques

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.


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Witten, and joined the Department of Computer Science at the University of Waikato as a lecturer on completion of his studies. He is now an associate 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. Hall holds a bachelor's degree in computing and mathematical sciences and a Ph.

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.


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He has published a number of articles on machine learning and data mining and has refereed for conferences and journals in these areas. Data Mining: Practical Machine Learning Tools and Techniques, Third Edition, offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations.

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This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors.

Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. The book will also be useful for professors and students of upper-level undergraduate and graduate-level data mining and machine learning courses who want to incorporate data mining as part of their data management knowledge base and expertise. Read more Read less. Review "The authors provide enough theory to enable practical application, and it is this practical focus that separates this book from most, if not all, other books on this subject.

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  7. Most helpful customer reviews on Amazon. June 22, - Published on Amazon. Verified Purchase. Its price is about right for a page machine learning textbook, and you don't even need to know that WEKA exists for the first pages.

    Top Machine Learning Tools and Frameworks for Beginners - Machine Learning Tutorial - Edureka

    I never read any of the WEKA stuff and got tons out of the textbook part. There's very little actual math or theory in this book. The average explanation amounts to "There's a technique called X, where you do this Problems mostly come from the lack of organization. Most of these are in Chapter 6, which is by far the most important chapter. For instance, this chapter begins with two or three pages describing what's going on in Figure 1.

    Each section of the chapter references its corresponding section in Chapter 4 a lot. The authors also assume that you memorized, in intimate detail, their examples in the first five pages because they keep referencing them in detail throughout the book. Finally, the explanations of a couple algorithms -- decision trees, in particular -- can get disorganized and confusing; however, these are exceptions to the rule. But, this is a good book. I got a lot of new ideas out of it for how to improve some the algorithms I work on, or for new things to try. December 14, - Published on Amazon.

    The book is really good to start learning machine learning and data mining. Pros - It doesn't jump into algorithms with mathematical details. It starts with what is it all about, what input and output look like in typical machine learning problems. The first chapter is about basics and latter one gives detail about these algorithms. How to normalize data, what happens your data have both categorical and numerical features, discretizing numerical features and so on.

    Authors are from the team who built Weka. He moved to New Zealand to pursue his Ph.

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    Witten, and joined the Department of Computer Science at the University of Waikato as a lecturer on completion of his studies. He is now an associate professor at the same institution.

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    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. Witten , Eibe Frank. Algorithmic methods at the heart of successful data mining—including tried and true techniques as well as leading edge methods Performance improvement techniques that work by transforming the input or output.

    Knowledge representation. The Explorer. Training and testing learning schemes. Clustering and association rules. Unsupervised instance filters. The Knowledge Flow interface.

    Data Mining: Practical Machine Learning Tools and Techniques (3rd ed.)

    Analyzing the results. The Weka machine learning workbench. Going through the code.