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Book details
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Author : Ian H. Witten
Pages : 664 pages
Publisher : Morgan
Kaufmann 2011-01-20
Language : English
ISBN-10 : 0123748569
ISBN-13 :
9780123748560
Book Synopsis
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 realworld 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-andtrue 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&Dprofessionals,
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 projectsOffers
concrete tips and techniques
for performance
improvement that work by
transforming the input or
output in machine learning
methodsIncludes
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