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Principles of Data Mining (Undergraduate Topics in Computer Science)

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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. 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. This second edition has been expanded to include additional chapters on using frequent pattern trees for Association Rule Mining, comparing classifiers, ensemble classification and dealing with very large volumes of data. Principles of Data Mining 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. Suitable 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.

Synopsis:

This book explains the principal techniques of data mining, for classification, association rule mining and clustering. Each topic is clearly explained and illustrated by detailed examples, with a focus on algorithms rather than mathematical formalism.

Table of Contents

Introduction to Data Mining.- Data for Data Mining.- Introduction to Classification: Naïve 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.- Dealing with Large Volumes of Data.- Ensemble Classification.- Comparing Classifiers.- Associate Rule Mining I.- Associate Rule Mining II.- Associate Rule Mining III.- Clustering.- Mining.- 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:
9781447148838
Author:
Bramer, Max
Publisher:
Springer
Subject:
Information Storage & Retrieval
Subject:
Associate Rule Mining
Subject:
Attribute Selection
Subject:
Classification
Subject:
Classifiers
Subject:
clustering
Subject:
Data mining
Subject:
Data Sets
Subject:
Decision Trees.
Subject:
Entropy
Subject:
Information storage and retrieval.
Subject:
Database management
Subject:
Artificial Intelligence (incl. Robotics)
Subject:
Programming techniques
Subject:
Computers-Reference - General
Copyright:
Edition Description:
2nd ed. 2013
Series:
Undergraduate Topics in Computer Science
Publication Date:
20130328
Binding:
TRADE PAPER
Language:
English
Pages:
454
Dimensions:
235 x 155 mm

Related Subjects

Computers and Internet » Artificial Intelligence » General
Computers and Internet » Computers Reference » General
Computers and Internet » Database » Design
Computers and Internet » Internet » Information
Computers and Internet » Software Engineering » Software Management

Principles of Data Mining (Undergraduate Topics in Computer Science) New Trade Paper
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Product details 454 pages Springer - English 9781447148838 Reviews:
"Synopsis" by , This book explains the principal techniques of data mining, for classification, association rule mining and clustering. Each topic is clearly explained and illustrated by detailed examples, with a focus on algorithms rather than mathematical formalism.
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