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Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series in Data Management Systems) (Englisch) Taschenbuch – 6. Januar 2011

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  • Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series in Data Management Systems)
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  • Pattern Recognition and Machine Learning (Information Science and Statistics)
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  • An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)
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Produktbeschreibungen

Pressestimmen

"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."- Dorian Pyle, Director of Modeling at Numetrics and an internationally known author of "Data Preparation for Data Mining" (Morgan Kaufmann, 1999) and "Business Modeling for Data Mining" (Morgan Kaufmann, 2003)

"This book would be a strong contender for a technical data mining course. It is one of the best of its kind."- Herb Edelstein, Principal, Data Mining Consultant, Two Crows Consulting.

"It is certainly one of my favorite data mining books in my library"- Tom Breur, Principal, XLNT Consulting, Tilburg, The Netherlands

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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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.
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Kommentar 33 Personen fanden diese Informationen hilfreich. War diese Rezension für Sie hilfreich? Ja Nein Feedback senden...
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This book provides a very pratical approach for WEKA users and gives an introduction in a large range of topics.
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Die hilfreichsten Kundenrezensionen auf Amazon.com (beta)

Amazon.com: HASH(0x9dbf3180) von 5 Sternen 69 Rezensionen
108 von 110 Kunden fanden die folgende Rezension hilfreich
HASH(0x9dc03e1c) von 5 Sternen Worthwhile Update to an Excellent Text 6. März 2011
Von Amazon Customer - 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.
28 von 28 Kunden fanden die folgende Rezension hilfreich
HASH(0x9dc08d2c) 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.
32 von 33 Kunden fanden die folgende Rezension hilfreich
HASH(0x9dc0bc24) 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.
32 von 34 Kunden fanden die folgende Rezension hilfreich
HASH(0x9dc08c48) 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
HASH(0x9dc0bc30) 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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