Es wird kein Kindle Gerät benötigt. Laden Sie eine der kostenlosen Kindle Apps herunter und beginnen Sie, Kindle-Bücher auf Ihrem Smartphone, Tablet und Computer zu lesen.

  • Apple
  • Android
  • Windows Phone
  • Android

Geben Sie Ihre Mobiltelefonnummer ein, um die kostenfreie App zu beziehen.

Kindle-Preis: EUR 40,41
inkl. MwSt.

Diese Aktionen werden auf diesen Artikel angewendet:

Einige Angebote können miteinander kombiniert werden, andere nicht. Für mehr Details lesen Sie bitte die Nutzungsbedingungen der jeweiligen Promotion.

An Ihren Kindle oder ein anderes Gerät senden

An Ihren Kindle oder ein anderes Gerät senden

Facebook Twitter Pinterest <Einbetten>
Data Mining:: Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series in Data Management Systems) von [Witten, Ian H., Eibe Frank, Mark A. Hall]
Anzeige für Kindle-App

Data Mining:: Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series in Data Management Systems) 3 , Kindle Edition

5.0 von 5 Sternen 2 Kundenrezensionen

Alle 4 Formate und Ausgaben anzeigen Andere Formate und Ausgaben ausblenden
Preis
Neu ab Gebraucht ab
Kindle Edition
"Bitte wiederholen"
EUR 40,41

Juni-Aktion: Englische eBooks stark reduziert
Entdecken Sie unsere Auswahl an englischen eBooks aus verschiedenen Genres für je 1,49 EUR. Die aktuelle Aktion läuft noch bis zum 30. Juni 2017.

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"

"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



-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

Kurzbeschreibung

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. 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 is targeted at information systems practitioners, programmers, consultants, developers, information technology managers, specification writers, data analysts, data modelers, database R&D professionals, data warehouse engineers, data mining professionals. 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.

  • Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects
  • Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods
  • 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

Produktinformation

  • Format: Kindle Edition
  • Dateigröße: 8385 KB
  • Seitenzahl der Print-Ausgabe: 664 Seiten
  • Verlag: Morgan Kaufmann; Auflage: 3 (22. Dezember 2010)
  • Verkauf durch: Amazon Media EU S.à r.l.
  • Sprache: Englisch
  • ASIN: B004H1TB1W
  • Text-to-Speech (Vorlesemodus): Aktiviert
  • X-Ray:
  • Word Wise: Nicht aktiviert
  • Verbesserter Schriftsatz: Nicht aktiviert
  • Durchschnittliche Kundenbewertung: 5.0 von 5 Sternen 2 Kundenrezensionen
  • Amazon Bestseller-Rang: #248.287 Bezahlt in Kindle-Shop (Siehe Top 100 Bezahlt in Kindle-Shop)

  •  Ist der Verkauf dieses Produkts für Sie nicht akzeptabel?

Welche anderen Artikel kaufen Kunden, nachdem sie diesen Artikel angesehen haben?

Kundenrezensionen

5.0 von 5 Sternen
5 Sterne
2
4 Sterne
0
3 Sterne
0
2 Sterne
0
1 Stern
0
Beide Kundenrezensionen anzeigen
Sagen Sie Ihre Meinung zu diesem Artikel

Top-Kundenrezensionen

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... ›
Kommentar 35 Personen fanden diese Informationen hilfreich. War diese Rezension für Sie hilfreich? Ja Nein Feedback senden...
Vielen Dank für Ihr Feedback.
Wir konnten Ihre Stimmabgabe leider nicht speichern. Bitte erneut versuchen
Missbrauch melden
Format: Taschenbuch Verifizierter Kauf
This book provides a very pratical approach for WEKA users and gives an introduction in a large range of topics.
Kommentar War diese Rezension für Sie hilfreich? Ja Nein Feedback senden...
Vielen Dank für Ihr Feedback.
Wir konnten Ihre Stimmabgabe leider nicht speichern. Bitte erneut versuchen
Missbrauch melden

Die hilfreichsten Kundenrezensionen auf Amazon.com (beta) (Kann Kundenrezensionen aus dem "Early Reviewer Rewards"-Programm beinhalten)

Amazon.com: 4.2 von 5 Sternen 77 Rezensionen
2 von 2 Kunden fanden die folgende Rezension hilfreich
4.0 von 5 Sternen Good for overview and intuition 22. Juni 2014
Von Andrew - Veröffentlicht auf Amazon.com
Format: Taschenbuch Verifizierter Kauf
First of all, I would advise to think of this as a 400-page book with a WEKA appendix. Its price is about right for a 400-page machine learning textbook, and you don't even need to know that WEKA exists for the first 400 pages. 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... it has a couple problems, but you could try fixing them in these ways." It's great for getting a lot of machine learning and data mining ideas in your head without having to get confused by learning the math behind them.

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 1.3 from two-hundred pages earlier. 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. It's great to have explanations of these machine learning algorithms and concepts that give you an intuition for what their goal/purpose is without going into too much detail about why they work -- there are ten other books for that.
5 von 5 Kunden fanden die folgende Rezension hilfreich
4.0 von 5 Sternen good textbook to start machine learning / data mining 14. Dezember 2011
Von Sefa - Veröffentlicht auf Amazon.com
Format: Taschenbuch Verifizierter Kauf
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.
- One point that I really liked is that the book gives algorithms in two chapters (chapter 4 and 6). The first chapter is about basics and latter one gives detail about these algorithms.
- It also covers well that I think it is mostly ignored by other books/tutorials: practical issues. How to normalize data, what happens your data have both categorical and numerical features, discretizing numerical features and so on.
- If you consider using Weka, you should have this book. Authors are from the team who built Weka. For each algorithm described in the book, corresponding names of implementations in Weka are given too. With the book it is easier to understand parameters of Weka implementations of algorithms. Also last part of the book is like extensive Weka tutorial.

Cons
- In a few points, the book contains unnecessary details, although it is not the case for overall of the book. One of such things that I remember is chapter 4.7. The book spends 5 whole pages to how to find nearest neighbor efficiently (not-easy stuff), which I think it is really implementation detail. Instead of it, it could explain what nearest neighbor is, or something else.
- The part about Weka has several figures, mostly Weka screen shots. It was difficult to follow these figures, because of black-white screen shots. I think these figures should be in color in the next edition, which will make much easier to follow.
5.0 von 5 Sternen https: //www. amazon. com/review/review-your-purchases/ref=pe_6680_237317010_cm_1_star1? _encoding=UTF8&asins=0123748569%3A1%2C& 16. Mai 2017
Von Amazon Customer - Veröffentlicht auf Amazon.com
Format: Taschenbuch Verifizierter Kauf
https://www.amazon.com/review/review-your-purchases/ref=pe_6680_237317010_cm_1_star1?_encoding=UTF8&asins=0123748569%3A1%2C&channel=ec_phy&crAuthToken=gLKb6AQh1rxaq%2BzzMfDCwQVH3hWKOjAEP%2FiK4KMAAAAJAAAAAFkaSMxyYXcAAAAA&customerId=A3DHO0P12FO1D#https://www.amazon.com/review/review-your-purchases/ref=pe_6680_237317010_cm_1_star1?_encoding=UTF8&asins=0123748569%3A1%2C&channel=ec_phy&crAuthToken=gLKb6AQh1rxaq%2BzzMfDCwQVH3hWKOjAEP%2FiK4KMAAAAJAAAAAFkaSMxyYXcAAAAA&customerId=A3DHO0P12FO1D#Data Mining: Practical Machine Learning Tools and Techniques, Third Edition (Morgan Kaufmann Series in Data Management Systems)
5 von 5 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen You will need some time but it is worth the investment 11. Dezember 2011
Von Jochen Albrecht - Veröffentlicht auf Amazon.com
Format: Taschenbuch Verifizierter Kauf
To get the most out of this book, you need to either be a statistician, AI professional, or be willing to invest some time. But: if you commit yourself, then this book goes a long way to substitute for a graduate-level course on data mining. Don't get me wrong - it is not written with an academic audience in mind; as a matter of fact, it is unusually rich with application examples. But there is a lot to digest conceptually and many of the examples are quite involved. As such, it addresses the opposite end the O'Reilly series of how-to books. This one gets you up to speed with one of if not the best software package for data mining in all its many facets. With Weka and 'R', you have the tools to tackle many of the World's problems, and this book is the best introduction to one part of the duo.
2 von 2 Kunden fanden die folgende Rezension hilfreich
4.0 von 5 Sternen A good introduction 27. Juni 2011
Von Someone - Veröffentlicht auf Amazon.com
Format: Taschenbuch Verifizierter Kauf
Good:

Provides a good overview of the field as well as basic concepts in everyday language. It tries to explain any jargon that it introduces. I'm going to disagree with a reviewer. The book is not WEKA focused and is useful for people even if they are not going to use WEKA. Even if the book had a strong focus on WEKA, I don't see what's wrong given that WEKA is free and open source and widely used for machine learning and data mining. I'll elaborate more on this subject in the Bad section.

Provides both overviews as well as more detailed discussions (such as potential applications) related to various types of commonly used algorithms. I felt the diagrams it presents were key to understanding the concepts.

Applies the concepts using a library called WEKA.

Bad:

I didn't think the book focused enough on application. It's great to learn theory, but personally I don't learn a subject very well if I don't have enough opportunities to apply it. I totally disagree with another reviewer. I felt that the book should have had MORE content related to WEKA. Yes, WEKA is free and open source but there is so little documentation written on it compared to other Java libraries and frameworks. More content on it would have been better since there isn't a definitive book on it that didn't solely focus on the GUI application portion of WEKA. Besides there are plenty of other good books that focus on data mining and machine learning. This is the only one for WEKA.
Waren diese Rezensionen hilfreich? Wir wollen von Ihnen hören.
click to open popover