- Taschenbuch: 371 Seiten
- Verlag: Morgan Kaufmann (30. November 1999)
- Sprache: Englisch
- ISBN-10: 1558605525
- ISBN-13: 978-1558605527
- Größe und/oder Gewicht: 18,4 x 2,5 x 23,5 cm
- Durchschnittliche Kundenbewertung: 5 Kundenrezensionen
- Amazon Bestseller-Rang: Nr. 315.609 in Fremdsprachige Bücher (Siehe Top 100 in Fremdsprachige Bücher)
- Komplettes Inhaltsverzeichnis ansehen
Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations (Morgan Kaufmann Series in Data Management Systems) (Englisch) Taschenbuch – 30. November 1999
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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.
"This is a milestone in the synthesis of data mining, data analysis, information theory and machine learning."-Jim Gray, Microsoft Research, USAAlle Produktbeschreibungen
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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.
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.
For those already conversant in machine learning, it contains a wealth of practical techniques for improving and analysing results. I expect to use it often in the course of my research.
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