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Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series in Data Management Systems) [Englisch] [Taschenbuch]

Ian H. Witten , Eibe Frank , Mark A. Hall
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Kurzbeschreibung

3. Februar 2011 Morgan Kaufmann Series in Data Management Systems
"Data Mining: Practical Machine Learning Tools and Techniques" 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. 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. It provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects. It offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods. It includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks-in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization.

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Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series in Data Management Systems) + Pattern Recognition and Machine Learning (Information Science and Statistics)
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Produktinformation

  • Taschenbuch: 664 Seiten
  • Verlag: Morgan Kaufmann; Auflage: 3. Auflage. (3. Februar 2011)
  • Sprache: Englisch
  • ISBN-10: 0123748569
  • ISBN-13: 978-0123748560
  • Größe und/oder Gewicht: 19,1 x 3,4 x 23,5 cm
  • Durchschnittliche Kundenbewertung: 5.0 von 5 Sternen  Alle Rezensionen anzeigen (2 Kundenrezensionen)
  • Amazon Bestseller-Rang: Nr. 21.001 in Fremdsprachige Bücher (Siehe Top 100 in Fremdsprachige Bücher)

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Produktbeschreibungen

Pressestimmen

"Co-author Witten is the author of other well-known books on data mining, and he and his co-authors of this book excel in statistics, computer science, and mathematics. Their in- depth backgrounds and insights are the strengths that have permitted them to avoid heavy mathematical derivations in explaining machine learning algorithms so they can help readers from different fields understand algorithms. I strongly recommend this book to all newcomers to data mining, especially to those who wish to understand the fundamentals of machine learning algorithms."--INFORMS Journal of Computing "The third edition of this practical guide to machine learning and data mining is fully updated to account for technological advances since its previous printing in 2005 and is now even more closely aligned with the use of the Weka open source machine learning, data mining and data modeling application. Beginning with an introduction to data mining, the volume explores basic inputs, outputs and algorithms, the implementation of machine learning schemes and in-depth exploration of the many uses of the Weka data analysis software. Numerous illustration, tables and equations are included throughout and additional resources are available through a companion website. Witten, Frank and Hall are academics with the department of computer science at the University of Waikato, New Zealand, the home of the Weka software project."--Book News, Reference & Research "I would recommend this book to anyone who is getting started in either data mining or machine learning and wants to learn how the fundamental algorithms work. I liked that the book slowly teaches you the different algorithms piece by piece and that there are also a lot of examples. I plan on taking a machine learning course this upcoming fall semester and feel that the book gave me great insight that the course will be based on mathematics more than I had originally expected. My favorite part of the book was the last chapter where it explains how you can solve different practical data mining scenarios using the different algorithms. If there were more chapters like the last one, the book would have been perfect. This book might not be that useful if you do not plan on using the Weka software or if you are already familiar with the various machine learning algorithms. Overall, Data Mining: Practical Machine Learning Tools and Techniques is a great book to learn about the core concepts of data mining and the Weka software suite."-- ACM SIGSOFT Software Engineering Notes "This book is a must-read for every aspiring data mining analyst. Its many examples and the technical background it imparts would be a unique and welcome addition to the bookshelf of any graduate or advanced undergraduate student. The book is written for both academic and application-oriented readers, and I strongly recommend it to any reader working in the area of machine learning and data mining."--Computing Reviews.com

Synopsis

Like the popular second edition, "Data Mining: Practical Machine Learning Tools and Techniques" 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. Inside, you'll learn all you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining, including both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. Complementing the book is a fully functional platform-independent open source Weka software for machine learning, available for free download. The book is a major revision of the second edition that appeared in 2005.While the basic core remains the same, it has been updated to reflect the changes that have taken place over the last four or five years.

The highlights for the updated new edition include completely revised technique sections; new chapter on Data Transformations, new chapter on Ensemble Learning, new chapter on Massive Data Sets, a new book release version of the popular Weka machine learning open source software (developed by the authors and specific to the Third Edition); new material on multi-instance learning; new information on ranking the classification, plus comprehensive updates and modernization throughout; and, all in all, approximately 100 pages of new material.Features of this title include: thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques; algorithmic methods at the heart of successful data mining, including tired and true methods as well as leading edge methods; performance improvement techniques that work by transforming the input or output; and, downloadable Weka, a collection of machine learning algorithms for data mining tasks, including tools for data pre-processing, classification, regression, clustering, association rules, and visualization in an updated, interactive interface.


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Buchdeckel | Copyright | Inhaltsverzeichnis | Auszug | Stichwortverzeichnis
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23 von 25 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen Finest Blend 24. April 2011
Von Dr. Christian Donninger TOP 1000 REZENSENT
Format:Taschenbuch|Verifizierter Kauf
Wie die Autoren im Vorwort erwähnen gibt es (nicht nur C.D.) zu diesem Thema entweder Evangelisierungs- oder die akademische Machine-Learning Literateratur auf der anderen Seite. "The gulf is rather wide".
Dieses Buch ist eine äusserst gelungene Mischung aus praktischer Anwendung und theoretischen Grundlagen. Es wird auf viele praktisch relevante Fragen eingegangen. Z.B. das gute Datenvorbehandlung in der Regel weit wichtiger ist als eine komplizierte theoretisch überlegene Methode. Siehe dazu auch [1].
Die Stärke des Buches ist: Die Autoren haben mit dem Weka Explorer ein praktisch verwendetes System geschrieben. M.E. sollten nur Leute ein Buch schreiben dürfen, die eine praktisch relevante Implementierung ihrer Idee vorweisen können. Damit siebt man automatisch galaktische Algorithmen aus. Ein galaktischer Algorithmus ist eine Methode, die in der Praxis nie verwendet wird, weil man ihre Wirksamkeit innerhalb der Lebenszeit unserer Galaxie niemals bemerken würde. Je nach Wissenschaftsjournal sind 75% bis 95% der publizierten Methoden galaktisch. (Siehe [2]).
Es bleiben in diesem Buch und auch in Weka noch immer genügend Methoden über. Die Autoren gliedern daher jedes Kapitel in einen durchgehenden Text ohne jede Literaturhinweise. Es ist wohltuend nicht ständig durch "for further details see ..." im Lesefluss gestört zu werden. Am Ende gibt es noch einen Further Reading Abschnitt. Aber auch da wird streng der Spreu vom Weizen getrennt.
Es wurde im Rahmen eines Machine-Learning Kongresses eine Liste der 10 wichtigsten Algorithmen erstellt (siehe [3]). Das Buch beschreibt 9 dieser 10 Algos im Detail.
Der letzte Abschnitt ist eine Art Weka Reference Manual. Ich habe nicht alles im Detail durchgelesen.
Lesen Sie weiter... ›
War diese Rezension für Sie hilfreich?
5.0 von 5 Sternen Highly recommended for WEKA users 30. Januar 2014
Format:Taschenbuch|Verifizierter Kauf
This book provides a very pratical approach for WEKA users and gives an introduction in a large range of topics.
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Amazon.com: 4.1 von 5 Sternen  46 Rezensionen
89 von 91 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen Worthwhile Update to an Excellent Text 6. März 2011
Von William B. Dwinnell IV - Veröffentlicht auf Amazon.com
Format:Taschenbuch|Vine Kundenrezension eines kostenfreien Produkts (Was ist das?)
Context for this review: I am a data miner with 20 years experience, and own the first edition of this book.

Good:
- Accessible writing style
- Broad coverage of algorithms and data mining issues, with an eye toward practical issues
- Needless technical trivia (derivations and the like) are avoided
- Algorithms are completely spelled out: A competent programmer should be able to turn these descriptions into functioning code.
- Third edition makes meaningful improvements on previous editions

Bad(ish):
- Approximately one-third of this book is now devoted to the WEKA data mining software. I have nothing against WEKA, and it is a good choice for a text such as this, since WEKA is free. In my opinion, though, this coverage consumes too many pages of this book.
- Data mining draws from a number of fields with separate roots (statistics, machine learning, pattern recognition, engineering, etc.), and many techniques go by multiple names. As with many other data mining books, this one does not always point out the aliases by which data mining methods are known.

The bottom line: This is still the best data mining text on the market.
22 von 22 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen My favorite practical machine learning book 4. September 2011
Von Scott C. Locklin - Veröffentlicht auf Amazon.com
Format:Taschenbuch|Vine Kundenrezension eines kostenfreien Produkts (Was ist das?)
There exists a couple of classics of Machine learning, with various strengths and weaknesses. "The elements of statistical learning" by Hastie and company. Bishop's book, "Pattern Recognition and Machine Learning." And now, this book, "Data Mining." I'd say this is the most practical of the three books. The other two I mentioned are oriented towards theoretical underpinnings, and cataloging the rich zoology of machine learning techniques. This one tells you how to get stuff done. Lots of practical ideas on discretization, denoising, data preparation and performance characterization. It even has practical advice on things you really need an expert opinion on: for example, when using data folding techniques for cross validation ... what is a good number of folds to use? This book will tell you. It's like having a couple of seasoned experts looking over your shoulder when you're trying to get things done. It had a detailed recipe in it for something I really needed to solve... and their recipe worked!
While the subject matter is similar to the Bishop and Hastie books: what this most reminded me of was the classic physics text, "Numerical recipes." It's all very well having a good theoretical understanding of the techniques you're using. It's vastly more important to have advice on using them properly. This is that book; uniquely so, thus far, in my experience.
It's also a brilliant manual for their Weka machine learning environment, which is incredibly useful. I don't use the Weka UI, but I have called upon Weka as a library extension to the R programming environment. Mostly because of this book: it's both a recipe book and a map to a large collection of recipes you can use to solve your machine learning problems.

There isn't so much on time series applications, sadly, which is something I end up working with a lot. I'd love to see an extended chapter on the particular difficulties in using machine learning techniques to mine and forecast time series.
28 von 29 Kunden fanden die folgende Rezension hilfreich
4.0 von 5 Sternen Applying Machine Learning to Data Mining problems 1. April 2011
Von owookiee - Veröffentlicht auf Amazon.com
Format:Taschenbuch|Vine Kundenrezension eines kostenfreien Produkts (Was ist das?)
The subtitle of the book should really be emphasized more: Practical Machine Learning Tools and Techniques. This isn't a book about adhoc SQL queries and database statistics, it is about tools to discover relationships you didn't know you were looking for. Much of the book shows how to handle knowledge formation and representation, statistical modeling and projections. The one critique I have in regard is that much of the algorithm breakdowns are done in prose rather than true pseudocode.

I would like to echo other reviews that point out the text focuses on WEKA, and the authors indicate this is by intent. Though they do give much generic information, at some point you have to pick a horse to hitch your carriage to, and an established open-source project in Java is probably most widely accessible. Their coverage of WEKA claims 50% more features than the 2nd ed. and indeed it consumes half the book. I feel this is a good thing, as it lends great practicality to the book, allowing you to dig right in and get something actually done.

There are some additions to the 3rd ed. that modernize the book a bit. Showing how data can be reidentified (and the ethical implications) is pertinent to today's HIPAA-regulated medical environments. They also touch on web and ubiquitous mining, reflecting our growing foray into non-traditional cloud sources of information.
28 von 30 Kunden fanden die folgende Rezension hilfreich
4.0 von 5 Sternen Mixed Opinion 28. April 2011
Von GX - Veröffentlicht auf Amazon.com
Format:Taschenbuch|Vine Kundenrezension eines kostenfreien Produkts (Was ist das?)
Fantastic book if you need to use WEKA; probably the best recommendation available.

If, however, you're not going to be using WEKA then the book is still valuable, but I challenge the true 'practicality' of it. The content is thorough but perhaps more academically oriented than as industry focused as I would have liked. The author keeps it very accessible, particularly as far as mathematics and statistics go. While this might make the book a little more long winded - in my view it makes it a far easier to get into the groove and allows you to read it like a book.

* Highly recommended for WEKA users
* For others users I suggest you look through to see if it will really be helpful before plunking down the cash
6 von 6 Kunden fanden die folgende Rezension hilfreich
4.0 von 5 Sternen Concept over code 16. Mai 2011
Von Stratiotes Doxha Theon - Veröffentlicht auf Amazon.com
Format:Taschenbuch|Vine Kundenrezension eines kostenfreien Produkts (Was ist das?)
If you are looking for a simple how-to book that gives you a lot of sample source code, this is not for you. If you want to learn the concepts and theoretical underpinnings of various algorithms and techniques, this is a great place to start. The authors clearly stress the concepts of data mining that can be applied to a variety of specific applications. This is a must have volume for anyone wanting to truly understand the theories and concepts behind the various approaches to data mining and the tradeoffs involved with each approach. Those with a background in artificial intelligence will have an easier time getting through this material but such a background is not necessary to gain a solid foundation in the topics. It is well written and organized for self-study. But it may be a little intimidating for some beginners.
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