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Principles of Data Mining (Undergraduate Topics in Computer Science)

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Principles of Data Mining (Undergraduate Topics in Computer Science) Cover

 

Synopses & Reviews

Publisher Comments:

Data Mining, the automatic extraction of implicit and potentially useful information from data, is increasingly used in commercial, scientific and other application areas. This book explains and explores 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 & explanations of the algorithms given. 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 the general reader develop the necessary understanding to use commercial data mining packages discriminatingly, as well as enabling the advanced reader or academic researcher 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.

Synopsis:

This book explores 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.

Synopsis:

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.

Table of Contents

Introduction to Data Mining.- Data for Data Mining.- Introduction to Classification: Naive Bayes and Nearest Neighbour.- Using Decision Trees for Classification.- Decision Tree Induction: Using Entropy for Attribute Selection.- Decision Tree Induction: Using Frequency Tables for Attribute Selection.- Estimating the Predictive Accuracy of a Classifier.- Continuous Attributes.- Avoiding Overfitting of Decision Trees.- More about Entropy.- Inducing Modular Rules for Classification.- Measuring the Performance of a Classifier.- Association Rule Mining I.- Association Rule Mining II.- Clustering.- Text Mining.- References.- Appendix A: Essential Mathematics.- Appendix B: Datasets.- Appendix C: Sources of Further Information.- Appendix D: Glossary and Notation.- Appendix E: Solutions to Self-assessment Exercises.- Index.

Product Details

ISBN:
9781846287657
Author:
Bramer, Max
Publisher:
Springer
Author:
Bramer, M. A.
Subject:
Data mining
Subject:
Information Storage & Retrieval
Subject:
Programming - General
Subject:
Database Management - General
Subject:
Database Management - Database Mining
Subject:
System Administration - Storage & Retrieval
Subject:
Computers-Reference - General
Subject:
Artificial Intelligence
Subject:
Databases
Subject:
Knowledge Discovery
Subject:
Information storage and retrieval.
Subject:
Database management
Subject:
Artificial Intelligence (incl. Robotics)
Subject:
Programming techniques
Copyright:
Edition Description:
1st Edition.
Series:
Undergraduate Topics in Computer Science
Publication Date:
20070328
Binding:
TRADE PAPER
Language:
English
Illustrations:
Y
Pages:
354
Dimensions:
235 x 178 mm 1250 gr

Related Subjects

Computers and Internet » Artificial Intelligence » General
Computers and Internet » Computers Reference » General
Computers and Internet » Internet » Information
Computers and Internet » Software Engineering » Programming and Languages

Principles of Data Mining (Undergraduate Topics in Computer Science) New Trade Paper
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Product details 354 pages Springer - English 9781846287657 Reviews:
"Synopsis" by , This book explores 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.
"Synopsis" by , 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.
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