Call number:
M 15.0104
Description / Table of Contents:
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.
Type of Medium:
Monograph available for loan
Pages:
xxxiiii, 629 pages
,
illustrations, diagrams
,
24 cm
Edition:
3rd ed.
ISBN:
978-0-12-374856-0
Series Statement:
Morgan Kaufmann series in data management systems
Classification:
Informatics
Language:
English
Note:
Part I: Introduction to Data Mining
Chapter 1 - What's It All About?
Chapter 2 - Input: Concepts, Instances, and Attributes
Chapter 3 - Output: Knowledge Representation
Chapter 4 - Algorithms: The Basic Methods
Chapter 5 - Credibility: Evaluating What's Been Learned
Part II: Advanced Data Mining
Chapter 6 - Implementations: Real Machine Learning Schemes
Chapter 7 - Data Transformations
Chapter 8 - Ensemble Learning
Chapter 9 - Moving on: Applications and Beyond
Part III: The Weka Data Mining Workbench
Chapter 10 - Introduction to Weka
Chapter 11 - The Explorer
Chapter 12 - The Knowledge Flow Interface
Chapter 13 - The Experimenter
Chapter 14 - The Command-Line Interface
Chapter 15 - Embedded Machine Learning
Chapter 16 - Writing New Learning Schemes
Chapter 17 - Tutorial Exercises for the Weka Explorer
Location:
Upper compact magazine
Branch Library:
GFZ Library
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