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Introduction to Data Mining: Pearson New International Edition (Anglais) Broché – 17 juillet 2013

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Présentation de l'éditeur

Introduction to Data Mining presents fundamental concepts and algorithms for those learning data mining for the first time. Each concept is explored thoroughly and supported with numerous examples. The text requires only a modest background in mathematics.  

Each major topic is organized into two chapters, beginning with basic concepts that provide necessary background for understanding each data mining technique, followed by more advanced concepts and algorithms.

 

 

Quotes

This book provides a comprehensive coverage of important data mining techniques. Numerous examples are provided to lucidly illustrate the key concepts.

-Sanjay Ranka, University of Florida

 

In my opinion this is currently the best data mining text book on the market. I like the comprehensive coverage which spans all major data mining techniques including classification, clustering, and pattern mining (association rules).

-Mohammed Zaki, Rensselaer Polytechnic Institute

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Format: Relié
Sans doute LE livre à lire en premier pour étudier la fouille de données. Couverture très complète de la matière (avec bibliographie très documentée pour l'approfondissement), la qualité pédagogique US, nombreux exercices bien conçus. L'anglais utilisé est très accessible. Attention : télécharger les (7 pages PDF) d'erratas et les fichiers PowerPoint sur le site de l'éditeur. Ces erreurs sont dues à la composition (inversions d'indice "i" et "j", etc) et seront sans doute corrigées dans une nouvelle édition; les auteurs ne sont pas en cause. Après lecture, vous serez assez opérationnels pour utiliser un logiciel tel que Tanagra (voir Univ. Lyon). Ce livre est un premier choix.
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Commentaires client les plus utiles sur Amazon.com (beta)

Amazon.com: 4.0 étoiles sur 5 41 commentaires
5 internautes sur 5 ont trouvé ce commentaire utile 
2.0 étoiles sur 5 Terrible Paper Quality! 24 février 2014
Par José Angel Daza - Publié sur Amazon.com
Format: Relié Achat vérifié
I am incredibly disappointed with this book. Of course the content is amazing and is a MUST for everyone who wants to start studying this particular field. However I think that if someone still pays almost double price for the paperback version is because he/she wants a good quality material to consult, at least that was my case, and I must say that the paper is so thin that is impossible even to use a highlighter. I expected a lot more for a book whose price is so huge...
4 internautes sur 4 ont trouvé ce commentaire utile 
4.0 étoiles sur 5 A Reasonable Academic Approach to DM 18 avril 2012
Par Sojournalist - Publié sur Amazon.com
Format: Relié Achat vérifié
We used this book in a class which was my first academic introduction to data mining.

The book's strengths are that it does a good job covering the field as it was around the 2008-2009 timeframe. Included are discussions of exploring data, classification, clustering, association analysis, cluster analysis, and anomaly detection. Additional bonus appendices cover some elements of linear algebra, dimensionality reduction, probability and statistics, regression analysis, and optimization, in case those concepts are fuzzy for the student. They're by no means thorough enough to learn the topic, merely to remind the reader of salient points they should remember.

I liked the structure of the book, with each analysis topic being divided into a basic concepts and algorithms chapter, followed by an additional issues and algorithms chapter.

I liked that when algorithms were presented, they were presented as pseudocode rather than in any particular language.

What I did not like is that separating the concepts from their applications created a bit too much distance for those wanting to apply these concepts. In our class, we were using a tool called Weka, which provides reference implementations of various data mining algorithms in Java, and sometimes it was difficult to tell what we should learn from the results of our experiments. The book did not discuss this very deeply, and certainly not against the types of results that we were getting from our application.

During the course, because I knew we would be relying on Weka, I purchased a copy of ISBN-10: 0123748569 http://www.amazon.com/Data-Mining-Practical-Techniques-Management/dp/0123748569/ref=pd_bxgy_b_text_b, which was written by the group that maintains Weka. I found their book to be helpful while I ran the Weka tool, and I was able to use it to develop command line use of the tool and solve some memory management problems. This book also covers much the same ground, although from a bit more practical perspective.

Later, because I'm interested in data mining in a large database environment, I purchased ISBN-10: 0123814790 http://www.amazon.com/Data-Mining-Concepts-Techniques-Management/dp/0123814790/ref=pd_bxgy_b_text_c, which is much more focused on the "how" of data mining, to include describing the use of data cubes and the necessities of processing it using data mining algorithms.

I cannot complain about Tan's book, just that I wished it had slightly more thorough explanations of what one should learn as data mining is certainly an iterative process. If you're interested in Weka, I recommend the Witten book, and if you're new to data modeling as well, I recommend the Han book.
8 internautes sur 8 ont trouvé ce commentaire utile 
2.0 étoiles sur 5 Not that useful 24 juillet 2011
Par Mattheos Protopapas - Publié sur Amazon.com
Format: Relié Achat vérifié
Although this book is considered as a standard introductory textbook for data mining classes, in my view it has limited scope. Key issues such as the logic (and perhaps some theory) behind classification and clustering techniques is not presented thoroughly, while there is an extended presentation of association analysis. This is in line with the research interests of the authors of course ( at least that is what I concluded by viewing the reference lists at the end of the chapters - the authors have published extensively in this field). The problem is that association rules are reported by other sources to be less useful than newest algorithms such as collaborative filtering. No coverage on regression exists in the book as well. So in overall I believe there are more useful books to introduce someone on this very interesting and fun field!
4 internautes sur 4 ont trouvé ce commentaire utile 
4.0 étoiles sur 5 Good for fundamental Data Mining Algorithms 8 août 2012
Par Mohid Farazi - Publié sur Amazon.com
Format: Relié Achat vérifié
Tan, Steinbach and Kumar did a good job. It is definitely a good read on Data Mining (DM). Pretty much all the fundamental DM algorithms are covered and explained in simplistic fashion. Most of the high level DM algorithms are basically off shoots of one or more of these fundamental DM algorithms, so it is imperative to have a clear understanding of them. This book is very helpful in that regard. It would have been nice if the book covered Support Vector Machine in more detail.

Undergraduate level Statistics and Linear Algebra knowledge is needed to understand some concepts covered in the book.

Good luck future miners.
4.0 étoiles sur 5 with lots of great references on where to go for deeper reading in ... 8 octobre 2016
Par Bob Savage - Publié sur Amazon.com
Format: Broché Achat vérifié
I really appreciated the annotated bibliography at the end of each chapter. Potential readers should be aware that this is a survey text, but if you are looking for introductory material on data mining, with lots of great references on where to go for deeper reading in selected topics, this is a good place to start.
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