Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems)

This is a robust and practical book explaining concept of data mining. I bought this as a textbook for Data Mining class at a grad school. Some chapters require quantitative background but in general it is relatively easy to read.

Needless to say, this is a classic book for data mining.It presents an extraordinary clear flow of materials. The essences of the difficult and challenging research publications are extractedand transformed into a domain even novice could catch the points.For practitioner who wish to get quick and broad-based overview, this book is for you.For students who are going to do research in this area, this is probably the place you should embark

As an experienced software engineer doing his first steps in the field of Data Mining, this book proved very useful in introducing the jargon, the basic concepts and the methods used in the field of Data Mining.The book is easy to read, filled with examples, and provides a comfortably paced (some would say slow) introduction to any mathematical / computational concepts required to understand any given subject.There book is lengthy, so it takes time to go over all the chapters. In addition, some more specialized concepts (such as data mining streams and social networks) are provided for free at the publisher's web-site.All in all - a good book for those who want a solid introduction to the field of Data Mining.

The books is very well written and explains the concepts in a very simple manner. It covers all the algorithms with good examples. A must for a person who wants to learn the concepts of data mining.

This is a great book for who is starting at data mining.It gives us a solid concept definition about data mining concepts, and common techniques.If you are willing to buy a theoretical book about data mining, this is the one.

Not only is this textbook excessively verbose, but it has separate indexes for figures, examples, and tables, which are shuffled without regard to the subsection in which they reside. Additionally, the book has horrendous layout and ordering issues. The text has excessive jargon and use of symbols, and a distinct lack of clear, concise language.Seriously, go elsewhere if you value your time or money.

The content of the book seems pretty good. I have only got up to about chapter four so far and it's easy to read and introduces material in a reasonably gentle manner. The three stars are due to the fact that I am using the electronic version, on a kindle app on an Android tablet, and it has some issues. It is mostly OK but suffers because quite a lot of the technical material is scanned rather than true font or vector which means that if you zoom in, the surrounding text gets bigger but the figures, formula and diagrams often don't. This is particularly troublesome for mathematical formulas which are sometimes scanned at quiet low resolution and very difficult to decipher, not great when they are already quite complex to understand.It's usable though, much more convenient in electronic form and I've saved myself about $50 by renting the book on Amazon for four months compared to buying it at the Uni bookshop.

The book is in an excellent state 👌

This is a great book if you are looking for a concept-driven textbook and strong overview of data mining. I find myself reaching for this book more than my more traditionally academic books when working with others. The people I work with want to understand how the techniques work in general; they aren't after impressive equations or technical language, impressive though it might sound. The more conversational tone is accessible and ease to dip back into for a reminder when used as a high level reference text.Overall, an especially good library addition for people working with non-stats people.

Fine package and book in good conditions 👍

It approaches the topics in a very easy way, I'm new on the topic, with a university background, not specially good a statistics, but this book is being easy to read & understand.

Come da titolo, è un buon libro. Fornisce una panoramica delle tecniche ( avanzate e basilari) di data minino. Molti capitoli sono spiegati davvero bene, altri un po' meno. Il problema è che un libro che lascia un po' l'amaro in bocca. In alcuni capitoli ti spiega molto da un punto di vista teorico, poco in quello pratico. Non per nulla l'ho usato come libro di supporto al famoso "mining of massive datasets"( anche se, ad esempio, a differenza di quest'ultimo, la parte di machine Learning ovvero SVM è molto più chiara qui). Inoltre, contiene spiegazioni di algoritmi che non ho visto in altri libri, quindi rimane un libro secondario da usare insieme ad altri testi.

Le livre était exactement tel que décrit dans la description (couverture légèrement endommagée, mais intérieur intact). Livraison Prime qui ne respectait pas le 2 jours, mais qui a été assez efficace tout de même.