2011 DataMiningPracticalMLTools

• (Witten et al., 2016) ⇒ Ian H. Witten, Eibe Frank, and Mark A. Hall. (2011). “Data Mining: Practical Machine Learning Tools and Techniques, Third Edition". Elsevier, 2011. ISBN: 0080890369, 9780080890364.

Subject Headings: Data Mining Text Book, Weka System.

Notes

• Google Books ID: bDtLM8CODsQC
• Book First Edition: (Witten & Frank, 2000)
• Book Second Edition: (Witten & Frank, 2005)
• Book Fourth Edition: (Witten et al., 2016)
• Book Website: https://www.cs.waikato.ac.nz/ml/weka/book.html
• Book Summary: http://dspace.fue.edu.eg/xmlui/bitstream/handle/123456789/3458/7003.pdf

Quotes

Book Overview

Data Mining: Practical Machine Learning Tools and Techniques, Third Edition, 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. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining.

Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research.

The book is targeted at information systems practitioners, programmers, consultants, developers, information technology managers, specification writers, data analysts, data modelers, database R&D professionals, data warehouse engineers, data mining professionals. The book will also be useful for professors and students of upper-level undergraduate and graduate-level data mining and machine learning courses who want to incorporate data mining as part of their data management knowledge base and expertise.

• Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects
• Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods
• Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks — in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2011 DataMiningPracticalMLTools	Ian H. Witten Eibe Frank Mark A. Hall			Data Mining: Practical Machine Learning Tools and Techniques, Third Edition			https://books.google.ca/books?id=bDtLM8CODsQC Google Books ID: bDtLM8CODsQC			2011