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Principles of data mining

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Principles of data mining

Record Type:	Electronic resources : Monograph/item
Title/Author:	Principles of data mining/ by Max Bramer.
Author:	Bramer, Max.
Published:	London :Springer London : : 2016.,
Description:	xv, 526 p. :ill., digital ;24 cm.
[NT 15003449]:	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 -- 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.
Contained By:	Springer eBooks
Subject:	Data mining. -
Online resource:	http://dx.doi.org/10.1007/978-1-4471-7307-6
ISBN:	9781447173076

Principles of data mining
Bramer, Max.

Principles of data mining[electronic resource] /by Max Bramer. - 3rd ed. - London :Springer London :2016. - xv, 526 p. :ill., digital ;24 cm. - Undergraduate topics in computer science,1863-7310. - Undergraduate topics in computer science..

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 -- 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.

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.

ISBN: 9781447173076

Standard No.: 10.1007/978-1-4471-7307-6doiSubjects--Topical Terms:

562972
Data mining.

LC Class. No.: QA76.9.D343

Dewey Class. No.: 006.312

Principles of data mining
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245 1 0 $a Principles of data mining $h [electronic resource] / $c by Max Bramer.
250 $a 3rd ed.
260 $a London : $c 2016. $b Springer London : $b Imprint: Springer,
300 $a xv, 526 p. : $b ill., digital ; $c 24 cm.
490 1 $a Undergraduate topics in computer science, $x 1863-7310
505 0 $a 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 -- 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.
520 $a 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.
650 0 $a Data mining. $3 562972
650 1 4 $a Computer Science. $3 626642
650 2 4 $a Information Storage and Retrieval. $3 761906
650 2 4 $a Database Management. $3 891010
650 2 4 $a Artificial Intelligence (incl. Robotics) $3 890894
650 2 4 $a Programming Techniques. $3 892496
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
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856 4 0 $u http://dx.doi.org/10.1007/978-1-4471-7307-6
950 $a Computer Science (Springer-11645)