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12th out of 33 books
—
4 voters
Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series in Data Management Systems)
The book is a major revision of the first edition that appeared in 1999. While the basic core remains the same, it has been updated to reflect the changes that have taken place over five years, and now has nearly double the references. The highlights for the new edition include thirty new technique sections; an enhanced Weka machine learning workbench, which now features a...more
Paperback, Second Edition, 525 pages
Published
June 1st 2005
by Morgan Kaufmann Publishers
(first published January 1st 2001)
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Loved this book. Although some parts were too slow, especially the first few chapters. Took a long time to explain concepts that could have been reduced a lot.
It is well worth sticking with it though; learnt some important concepts about data structures I hadn't come across before.
It is well worth sticking with it though; learnt some important concepts about data structures I hadn't come across before.
The book provides a good introduction to data mining algorithms including classification, clustering and association. It also provides practical hands-on exercises using an open source data mining tool developed by the authors called WEKA.
Pedantic to a fault. Otherwise, it's just a bunch of algorithms with analysis and discussion.
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recommended , used for data mining course at uni ..quite practical
A useful compendium of data mining techniques and accompaniment to the Weka data mining tool. This book is more an overview than a detailed treatise: there are descriptions but few precise algorithms; the maths is kept to a minimum and, where there is maths, it is often left mostly unexplained; the applications seem dated - there's little on mining large-scale scientific, medical or web data, for example; and issues of handling large scale data are skirted. Nevertheless, its scope is wide and it...more
Apr 01, 2010
Somkiat Chatchuenyot
marked it as to-read
Good to begin for web mining
Pretty darn good in terms of applied data-mining.
This big book has many sections that I used for my current Machine Learning online class: Applications, Knowledge Representation, Algorithms, Linear/Logistic Regression, Prediction, Classification, Clustering, and Cost Calculation. It also introduced me to the WEKA machine learning workbench, a set of free software tools that can be downloaded to implement many of the algorithms used in machine learning.
From the perspective of a computer scientist, this book is basically totally useless, as it leaves the reader with no idea how any of the algorithms really work. It might be helpful if you want to be able to use some machine learning software while avoiding having anything more than a cursory understand of how it works.
I like his stated approach to give readers a good feel for the different techniques of Machine Learning and what they can be used for.
I was looking for something not so theoretical, which is totally what it was to me. Practical to me means something with code...
A good medium level introduction to data mining. Written by the authors of WEKA which is used to apply the concepts in the book.
This link is to the WEKA software
http://www.cs.waikato.ac.nz/~ml/weka/...
http://www.cs.waikato.ac.nz/~ml/weka/...
Very mathy and deep, but also seems very practical and actionable so far.
Love the tool(Weka) the book is bad.
May 17, 2013
Pierre
marked it as to-read
May 11, 2013
Hazem Adel
marked it as to-read
May 10, 2013
Shahbaz
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May 03, 2013
Elena Jährig
marked it as to-read
Apr 30, 2013
Nemo
marked it as to-read
Apr 21, 2013
Viksit
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