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Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations
This book 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. Inside, you'll learn all you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining including both tr
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Paperback, 371 pages
Published
October 25th 1999
by Morgan Kaufmann Publishers
(first published October 11th 1999)
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(showing 1-30 of 1,341)
This is an excellent, but somewhat uneven, introduction to the field of machine learning, divided into three parts.
Part 1 is a good overview of the types of use cases, standard data sets, and algorithms. It provides more intuitive rather than technical explanations, though there is some math to get through. Reading just this section will definitely get you through any dinner party conversations about machine learning. I read through this twice, taking careful notes in my Moleskine (natch) the se ...more
Part 1 is a good overview of the types of use cases, standard data sets, and algorithms. It provides more intuitive rather than technical explanations, though there is some math to get through. Reading just this section will definitely get you through any dinner party conversations about machine learning. I read through this twice, taking careful notes in my Moleskine (natch) the se ...more
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
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I’ve been delaying picking up a proper data science book for a couple of years now and finally ran out of excuses not to do it. These days any moderately serious conversation/book about areas that I tend to follow - genetics, genomics, economic development, history, consciousness, prediction, uncertainty - requires a minimum grounding in statistics and/or machine learning. Thus, when a couple of weeks ago I had to look something up for a little work project I took the opportunity to read most of
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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.
Best introductory book on Data Mining in terms of concepts and practice. Not too academically but goal-driven and data-driven, which makes readers understand it easier.
WEKA is a great tool, although its part in this book is a little bit too much.
For those who needs more on theory perspective, I recommend the book "Introduction to Data Mining" (Pang-Ning Tan, Michael Steinbach, Vipin Kumar). But if you want to apply it on business without knowing a lot of mathematical backgrounds, you can look fo ...more
WEKA is a great tool, although its part in this book is a little bit too much.
For those who needs more on theory perspective, I recommend the book "Introduction to Data Mining" (Pang-Ning Tan, Michael Steinbach, Vipin Kumar). But if you want to apply it on business without knowing a lot of mathematical backgrounds, you can look fo ...more
I really, really wanted to like this book more than I did. After all, it was about a topic that I have great interest in, and describes a workbench application (Weka) that I can command-line access from my favorite programming environment (R, via RWeka).
The problem I was having with it is that its presentation, across the board, was incredibly wordy. They managed to make the interesting sound boring, and presented technical material with no grace whatsoever. The chapter on the Weka Explorer was ...more
The problem I was having with it is that its presentation, across the board, was incredibly wordy. They managed to make the interesting sound boring, and presented technical material with no grace whatsoever. The chapter on the Weka Explorer was ...more
While this book is an excellent overall summary of data mining technology, and it's an indispensable reference for using the Weka data mining software, it is not detailed enough, nor does it have enough examples, for an otherwise inexperienced novice data miner to be effective. If you come at it knowing a lot about statistics, probability, and modeling, you can get your knowledge rounded out with techniques and ideas you may not have experienced but make sense to you. If you don't bring such kno
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Jul 17, 2016
Aasim Waheed
rated it
really liked it
·
review of another edition
Shelves:
analytics-and-ml
Good book; a bit dry though; too much text with few diagrams makes it a difficult read
Did not perform any of the weka related exercises
I put this book down after reading the first 1/3 or so. It didn't seem to be that well done. I can't put my finger on exactly what i didn't like, but I'm moving on to something else.
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.
Nov 29, 2010
Brett Dargan
rated it
really liked it
·
review of another edition
Shelves:
machinelearning
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.
Jul 02, 2011
Darin
marked it as reference-only
This is a decent book at a high level. If you like a lot of theory, this isn't the book for you. The authors are also the authors of the machine learning tool Weka, which is briefly covered in this book.
Jun 27, 2013
Thomasreece
added it
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Very hands on/practical intro to the subject. For readers who want to start using ML techniques quickly and worry about theoretical considerations later.
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.
Pedantic to a fault. Otherwise, it's just a bunch of algorithms with analysis and discussion.
May 01, 2007
TK Keanini
marked it as to-read
·
review of another edition
Shelves:
must-buy-a-list,
machine-learning
This link is to the WEKA software
http://www.cs.waikato.ac.nz/~ml/weka/...
http://www.cs.waikato.ac.nz/~ml/weka/...
Sketchy.
USes WEKA
Seems like the author is just copying from other sources
USes WEKA
Seems like the author is just copying from other sources
Very mathy and deep, but also seems very practical and actionable so far.
Great reference book, with a good introduction to using the Weka suite.
Explains various ML schemes very well but limits only to WEKA.
recommended , used for data mining course at uni ..quite practical
Apr 21, 2016
Alexis
rated it
really liked it
·
review of another edition
Shelves:
machine-learning-data-science
just useful nerd stuff. /end epic review
Pretty darn good in terms of applied data-mining.
Love the tool(Weka) the book is bad.
Good to begin for web mining
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