Authors:
- 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
- Useful as a textbook and also for self-study
- Expanded third edition includes detailed descriptions of algorithms for classifying streaming data
- Includes supplementary material: sn.pub/extras
Part of the book series: Undergraduate Topics in Computer Science (UTICS)
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Table of contents (22 chapters)
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Front Matter
About this book
This book explains and explores the principal techniques of Data Mining, the automatic extraction of implicit and potentially useful information from data, which is increasingly used in commercial, scientific and other application areas. It focuses on classification, association rule mining and clustering.
Each topic is clearly explained, with a focus on algorithms not mathematical formalism, and is illustrated by detailed worked examples. The book is written for readers without a strong background in mathematics or statistics and any formulae used are explained in detail.
It can be used as a textbook to support courses at undergraduate or postgraduate levels in a wide range of subjects including Computer Science, Business Studies, Marketing, Artificial Intelligence, Bioinformatics and Forensic Science.
As an aid to self study, this book aims to help general readers develop the necessary understanding of what is inside the 'black box' so they can use commercial data mining packages discriminatingly, as well as enabling advanced readers or academic researchers to understand or contribute to future technical advances in the field.
Each chapter has practical exercises to enable readers to check their progress. A full glossary of technical terms used is included.
Authors and Affiliations
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University of Portsmouth School of Computing, Portsmouth, United Kingdom
Max Bramer
About the author
Max Bramer is Emeritus Professor of Information Technology at the University of Portsmouth, England, Vice-President of the International Federation for Information Processing (IFIP) and Chair of the British Computer Society Specialist Group on Artificial Intelligence.
He has been actively involved since the 1980s in the field that has since come to be called by names such as Data Mining, Knowledge Discovery in Databases, Big Data and Predictive Analytics. He has carried out many projects in the field, particularly in relation to automatic classification of data, and has published extensively in the technical literature. He has taught the subject to both undergraduate and postgraduate students for many years.
Some of Max Bramer’s other Springer publications include:
Research and Development in Intelligent Systems
Artificial Intelligence in Theory and Practice
Artificial Intelligence: an International Perspective
Logic Programming with PrologWeb Programming with PHP and MySQL
Bibliographic Information
Book Title: Principles of Data Mining
Authors: Max Bramer
Series Title: Undergraduate Topics in Computer Science
DOI: https://doi.org/10.1007/978-1-4471-7307-6
Publisher: Springer London
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer-Verlag London Ltd., part of Springer Nature 2016
eBook ISBN: 978-1-4471-7307-6Published: 09 November 2016
Series ISSN: 1863-7310
Series E-ISSN: 2197-1781
Edition Number: 3
Number of Pages: XV, 526
Number of Illustrations: 123 b/w illustrations
Topics: Information Storage and Retrieval, Database Management, Artificial Intelligence, Programming Techniques