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Data Mining: [Format Kindle]

Ian H. Witten , Eibe Frank , Mark A. Hall
5.0 étoiles sur 5  Voir tous les commentaires (1 commentaire client)

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Descriptions du produit

Revue de presse

"...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."

"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

Présentation de l'éditeur

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.



*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


Détails sur le produit

  • Format : Format Kindle
  • Taille du fichier : 8385 KB
  • Nombre de pages de l'édition imprimée : 664 pages
  • Editeur : Morgan Kaufmann; Édition : 3 (22 décembre 2010)
  • Vendu par : Amazon Media EU S.à r.l.
  • Langue : Anglais
  • ASIN: B004H1TB1W
  • Synthèse vocale : Activée
  • X-Ray :
  • Word Wise: Non activé
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  • Moyenne des commentaires client : 5.0 étoiles sur 5  Voir tous les commentaires (1 commentaire client)
  • Classement des meilleures ventes d'Amazon: n°124.452 dans la Boutique Kindle (Voir le Top 100 dans la Boutique Kindle)
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Commentaires client les plus utiles
0 internautes sur 1 ont trouvé ce commentaire utile 
5.0 étoiles sur 5 Parfait 16 janvier 2015
Format:Broché|Achat vérifié
Acheté dans un cadre professionnel, cet ouvrage est parfait. Tout "data scientist" devrait en avoir la version la plus récente dans sa bibliothèque !
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Amazon.com: 4.0 étoiles sur 5  54 commentaires
104 internautes sur 106 ont trouvé ce commentaire utile 
5.0 étoiles sur 5 Worthwhile Update to an Excellent Text 6 mars 2011
Par William B. Dwinnell IV - Publié sur Amazon.com
Format:Broché|Commentaire client Vine pour produit gratuit (De quoi s'agit-il?)
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.
27 internautes sur 27 ont trouvé ce commentaire utile 
5.0 étoiles sur 5 My favorite practical machine learning book 4 septembre 2011
Par Scott C. Locklin - Publié sur Amazon.com
Format:Broché|Commentaire client Vine pour produit gratuit (De quoi s'agit-il?)
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.
30 internautes sur 31 ont trouvé ce commentaire utile 
4.0 étoiles sur 5 Applying Machine Learning to Data Mining problems 1 avril 2011
Par owookiee - Publié sur Amazon.com
Format:Broché|Commentaire client Vine pour produit gratuit (De quoi s'agit-il?)
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.
31 internautes sur 33 ont trouvé ce commentaire utile 
4.0 étoiles sur 5 Mixed Opinion 28 avril 2011
Par GX - Publié sur Amazon.com
Format:Broché|Commentaire client Vine pour produit gratuit (De quoi s'agit-il?)
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 internautes sur 6 ont trouvé ce commentaire utile 
4.0 étoiles sur 5 Concept over code 16 mai 2011
Par Stratiotes Doxha Theon - Publié sur Amazon.com
Format:Broché|Commentaire client Vine pour produit gratuit (De quoi s'agit-il?)
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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