» » Principles of Data Mining (Undergraduate Topics in Computer Science)
eBook Principles of Data Mining (Undergraduate Topics in Computer Science) download
IT
Author: Max Bramer
ISBN: 1846287650
Subcategory: Computer Science
Pages 354 pages
Publisher Springer; 2007 edition (March 28, 2007)
Language English
Category: IT
Rating: 4.6
Votes: 159
ePUB size: 1363 kb
FB2 size: 1490 kb
DJVU size: 1212 kb
Other formats: doc docx lit mbr

eBook Principles of Data Mining (Undergraduate Topics in Computer Science) download

by Max Bramer


Principles of Data Mining explains and explores the principal techniques of Data Mining: for classification, association rule mining and clustering. Each topic is clearly explained and illustrated by detailed worked examples, with a focus on algorithms rather than mathematical formalism

Principles of Data Mining explains and explores the principal techniques of Data Mining: for classification, association rule mining and clustering. Each topic is clearly explained and illustrated by detailed worked examples, with a focus on algorithms rather than mathematical formalism. It is written for readers without a strong background in mathematics or statistics, and any formulae used are explained in detail

Undergraduate Topics in Computer Science. Principles of Data Mining. Artificial Intelligence in Theory and Practice.

Undergraduate Topics in Computer Science. Presents the principal techniques of data mining with particular emphasis on explaining and motivating the techniques used. Focuses on understanding of the basic algorithms and awareness of their strengths and weaknesses. Does not require a strong mathematical or statistical background. Artificial Intelligence: an International Perspective. Logic Programming with Prolog.

Max Bramer explains and explores the principal techniques of data mining, for classification, generation of association rules and clustering. ISBN13:9781846287657. Release Date:April 2007.

Principles of Data Mining. Book · January 2007 with 5,093 Reads. How we measure 'reads'. Undergraduate Topics in Computer Science ISSN 1863-7310. Isbn: 978-1-84628-765-7. cole Polytechnique, France and King’s College London, UK. Library of Congress Control Number: 2007922358. Digital Professor of Information Technology, University of Portsmouth, UK. Contents. Introduction to Data Mining.

Principles of Data Mining Series Foreword Preface Chapter 1. .

Published March 28, 2007 by Springer. Information storage and retrieval systems, Computer science, Artificial intelligence, Database management, Data mining.

Principles of Data Mining explains and explores the principal techniques of Data Mining: for classification, association rule mining and clustering. Published in Undergraduate Topics in Computer Science 2007. It is written for readers without a strong background in mathematics or statistics, and any formulae used are explained in detail.

This book explains the principal techniques of data mining: for classification, generation of association rules and clustering

This book explains the principal techniques of data mining: for classification, generation of association rules and clustering. It is written for readers without a strong background in mathematics or statistics and focuses on detailed examples and explanations of the algorithms given. Software Architecture in Action. Oquendo, . Leite, . Batista, T. (2016). This book presents a systematic model-based approach for software architecture according to three complementary viewpoints: structure, behavior, and execution.

add. Separate tags with commas, spaces are allowed. Use tags to describe a product . for a movie Themes heist, drugs, kidnapping, coming of age Genre drama, parody, sci-fi, comedy Locations paris, submarine, new york.

This book explains the principal techniques of data mining: for classification, generation of association rules and clustering. It is written for readers without a strong background in mathematics or statistics and focuses on detailed examples and explanations of the algorithms given. This will benefit readers of all levels, from those who use data mining via commercial packages, right through to academic researchers. The book aims to help the general reader develop the necessary understanding to use commercial data mining packages, and to enable advanced readers to understand or contribute to future technical advances. Includes exercises and glossary.
Akir
I bought this book for self-study and I am very surprised by the clarity and 'just-nice' amount of depth and coverage on the topic.
I pretty much able to understand most of the content by reading the book and wiki on the more difficult topic.
Butius
This book is an excellent introduction to data mining, concentrating primarily on decision tree induction. The material provided is presented clearly with no assumption of prior knowledge on the part of the reader. A weakness of the book is that it doesn't place the material provided within the larger context of machine learning, both in terms of breadth or depth. However, when used as a textbook the instructor could easily address this problem.
Mash
I'm a programmer with no great mathematical background (2nd year university maths and stats, decades old and mostly forgotten) trying to teach myself about machine learning, and I found this book to be at exactly the right level for me.

It's strongly oriented towards classifiers of one sort and another, and makes no claims to cover neural nets, genetic algorithms, genetic programming - but what it does cover it covers exceptionally clearly. I'd give it six stars out of five if it covered all aspects of machine learning, but I guess I can't have everything.

In terms of writing style and comprehensibility this is probably one of the best textbooks I have ever read. I wish that it covered much much more, but what it does do it does remarkably well.
Hasirri
This is an undergraduate introduction to data mining. The book doesn't go into details. It may be suitable for people who want to get a quick feel of the data mining field. People who need more details shall read more serious and comprehensive introductions. Overall I am giving 4 stars, because I liked it.