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Principles of Data Mining (3rd Edition)

Author(s):
Publisher:

Springer

Pages: 526
Further Actions:

Recommend to library

AVAILABLE FORMATS

Paperback - 9781447173069

17 November 2016

$69.99

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Ebook - 9781447173076

09 November 2016

$54.99

In stock

All prices are shown excluding Tax

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...

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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.

This expanded third edition includes detailed descriptions of algorithms for classifying streaming data, both stationary data, where the underlying model is fixed, and data that is time-dependent, where the underlying model changes from time to time - a phenomenon known as concept drift.

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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

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
Classifying Streaming Data
Classifying Streaming Data II: Time-dependent Data
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.

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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.

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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.

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