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Beginner Java @ Java Programming
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Java Index - Java Beginner's Guides :
Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations
by Ian H. Witten, Eibe Frank Paperback: 416 pages Dimensions (in inches): 0.82 x 9.12 x 7.29 Publisher: Morgan Kaufmann Publishers ISBN: 1558605525; 1st edition (October 11, 1999) Amazon.com: 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. Book 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. Features: • 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. Card catalog 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."--BOOK JACKET. About the Author: Ian H. Witten is professor of computer science at the University of Waikato in New Zealand. He is a fellow of the ACM and the Royal Society of New Zealand and a member of professional computing, information retrieval, and engineering associations in the UK, US, Canada, and New Zealand. He is coauthor of Managing Gigabytes (1999), The Reactive Keyboard (1992), and Text Compression (1990) and author of many journal articles and conference papers. Eibe Frank is a researcher in the Machine Learning group at the University of Waikato. He holds a degree in computer science from the University of Karlsruhe in Germany and is the author of several papers, both presented at machine learning conferences and published in machine learning journals. Customer Reviews Excellent introduction to data mining algorithms, February 7, 2000 Reviewer: Dean Abbott from San Diego, CA Witten and Frank have generated a book that is readable without eliminating all technical (yes, even mathematical!) descriptions of the key data mining algorithms. And they are up-to-date, including support vector machines and boosting. There are sufficient examples of the techniques to provide readers with a good feel for what each technique can accomplish. For example, how many books can provide a readable explanation of support vector machines? There are some quibbles, such as not including any discussion of neural networks (noted in Ch. 1 with another reference)--I believe it deserves some attention because of its widespread use. Additionally, future editions should include a least a brief summary of data preprocessing, input selection, feature creation, etc. But these are quibbles. The Java portion of the book is not of as much interest to me, but for those wishing to implement the algorithms, it provides a nice blueprint (from the code I looked at). For what they have undertaken, they have performed admirably, and I would highly recommend this book. You HAVE to read this book!, January 28, 2000 Reviewer: Bostjan Brumen from Tampere University of Technology at Pori, Finland & University of Maribor, Slovenia This book is THE best book I have read about data mining. And I have read most of them (see ISBNs: 0070057796, 0471253847, 0262560976, 0201403803, 0471179809, 013743980, 0137564120, 1558605290, 1558604030). It is fresh, clear, well balanced. If your native language is not English, then you should definetly read THIS book first. The feature that is the most important for me is "just enough statistics". That is, you can understand the processes & descriptions even if you have not wasted your life and youth studying statistics; what is needed of it to understand is given shortly and very well. Many other books are too deep or too shallow (like Berry's, which is a good introduction, but nothing more than that). If the rating was scaled 1-6 stars, I'd give this book a 10. Data mining technology power on 400 pages., February 28, 2002 Reviewer: Stefan Groschupf from Halle Deutschland It's difficult to get interesting. literature related to this theme. On the one hand there are some books written for managers, on the other hand there are some pretty mathematical books for academics. But this book is the best mix. You get an introduction to data mining and learn step by step from the basics up to the hard algorithm stuff with nice examples. There is a clear theme structure, and the deep technical sections are marked, so you can read what you are most interested in. The book describes not only one algorithm, but a lot of them and discusses plusses and minuses. Where it's necessary it uses simple diagrams to illustrate something, not so much that it looks like they want to fill the pages, like in other books. Best of all, the algorithms are implemented as an open source java software named "weka". This is my state of the art data mining tool. You can see the algorithms working and use the implementations for your ideas (like me). If you are hungry to learn more about one or the other thing, the book provides a literature list. For me this book was one of the best books in the last years, because it provides the best mix and gives you a fast but deep view in this theme. Our most popular book, August 16, 2001 Reviewer: Stuart Inglis from San Francisco Over the last 3 years our company has bought 15+ copies of this book and have given it to our new employees to help them gain a practical perspective when writing machine learning applications. The book is filled with practical insights and gems learnt from real data analysis experience. We have read almost all other data mining and machine learning books and have standardized on this book. Although the book is great, the software is amazing! Weka (the name of the software that is described in the book and is available for free) contains the largest collection of machine learning algorithms available in a coherent package. The software is written in Java and we have used it under a variety of platforms. Weka is incredible, and when combined with the well written explanations I have to give this book top marks. I'm looking forward to the next edition. |
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