Product Description
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 tried-and-true techniques of the past and Java-based methods at the leading edge of contemporary research. If you’re involved at any level in the work of extracting usable knowledge from large collections of data, this clearly written and effectively illustrated book will prove an invaluable resource.
Complementing the authors’ instruction is a fully functional platform-independent Java software system for machine learning, available for download. Apply it to the sample data sets provided to refine your data mining skills, apply it to your own data to discern meaningful patterns and generate valuable insights, adapt it for your specialized data mining applications, or use it to develop your own machine learning schemes.
* Helps you select appropriate approaches to particular problems and to compare and evaluate the results of different techniques.
* Covers performance improvement techniques, including input preprocessing and combining output from different methods.
* Comes with downloadable machine learning software: use it to master the techniques covered inside, apply it to your own projects, and/or customize it to meet special needs.Amazon.com Review
Data mining techniques are used to power intelligent software, both on and off the Internet. Data Mining: Practical Machine Learning Tools explains the magic behind information extraction in a book that succeeds at bringing the latest in computer science research to any IS manager or developer. In addition, this book provides an opportunity for the authors to showcase their powerful reusable Java class library for building custom data mining software.
This text is remarkable with its comprehensive review of recent research on machine learning, all told in a very approachable style. (While there is plenty of math in some sections, the authors’ explanations are always clear.) The book tours the nature of machine learning and how it can be used to find predictive patterns in data comprehensible to managers and developers alike. And they use sample data (for such topics as weather, contact lens prescriptions, and flowers) to illustrate key concepts.
After setting out to explain the types of machine learning models (like decision trees and classification rules), the book surveys algorithms used to implement them, plus strategies for improving performance and the reliability of results. Later the book turns to the authors’ downloadable Weka (rhymes with “Mecca”) Java class library, which lets you experiment with data mining hands-on and gets you started with this technology in custom applications. Final sections look at the bright prospects for data mining and machine learning on the Internet (for example, in Web search engines).
Precise but never pedantic, this admirably clear title delivers a real-world perspective on advantages of data mining and machine learning. Besides a programming how-to, it can be read profitably by any manager or developer who wants to see what leading-edge machine learning techniques can do for their software. –Richard Dragan
Topics covered: Data mining and machine learning basics, sample datasets and applications for data mining, machine learning vs. statistics, the ethics of data mining, generalization, concepts, attributes, missing values, decision tables and trees, classification rules, association rules, exceptions, numeric prediction, clustering, algorithms and implementations in Java, inferring rules, statistical modeling, covering algorithms, linear models, support vector machines, instance-based learning, credibility, cross-validation, probability, costs (lift charts and ROC curves), selecting attributes, data cleansing, combining multiple models (bagging, boosting, and stacking), Weka (reusable Java classes for machine learning), customizing Weka, visualizing machine learning, working with massive datasets, text mining, and e-mail and the Internet.
Introduction to Business Data Mining Book | David L. Olson Yong Shi NEW PB MHP | US $53.33 End Date: Tuesday Jun-05-2012 6:58:10 PDT Buy It Now for only: US $53.33 Buy it now | Add to watch list |
This is the worst book I have ever ordered from Amazon. It seems to me that the author does not know much on the practide. The books have make some points in inserting buzz words occasionaly, but no more than that. The rest is just full of words that you have no clue why they make it meaninglessly lengthy — just to make it long enough to be called a book?
Amazon User Rating: 1 / 5
Poor writing, often delves on irritating jokes and unimportant topics (for instance I didn’t buy this book to tell me about how cool javadoc is), fails to deliver complete mathematical background for the models, fails to give a good explanation on how to use Weka software.
Overall it’s a big black hole that’ll eat away a chunk of your time while providing a super low return in useful knowledge.
Can’t they write a few separate chapters that provide all the information you need and teach you a few algorithms instead of trying to be an encyclopedia and be so shallow as they are?
Academic, hard to follow, often references other books for critical info, poorly organized. Skip it while it’s not too late.
Amazon User Rating: 1 / 5
My goal when I purchased this book was to learn the fundametal techniques and algorithms of data mining, such as C4.5/C5.0 and other popular algorithms, after reading the book my goal is far from being reached, on the one hand the book is not will structured, it covers many topics but with a very weak logical connection among them, on the other hand there’s no complete and simple example that take the reader from A to Z illustrating step by step the basic concepts of reducing entropy, rules productions and pruning etc…, finally there’s no design explanation of the downloadable code that can give a global view of the “software” architecture and it’s building blocks, leaving the reader confused and wishing he saved he’s money!.
Amazon User Rating: 2 / 5
I have read machine learning writed by Tom M. Mitchell and also I have read Data Mining Concepts and Techniques writed by J. Han and M. Kamber. Both text books is very useful for someone who want to get concept of a modern data analysis approaches. But, however, to understant about that clearly, you should read this book also because the example and author’s form writing is so good and nice, very easy to understant.
Amazon User Rating: 5 / 5
This book covers data mining at a serious level, including essential material on testing and a broad array of techniques. It is written for practitioners and provides clear explanation of included topics. Easily one of the best 5 books on data mining currently available.
Note that this book has moved on to a second edition.
Amazon User Rating: 5 / 5