國立虎尾科技大學 |

Principles of Data Mining

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Principles of Data Mining/ by Max Bramer.
作者:	Bramer, Max.
面頁冊數:	XV, 526 p. 123 illus.online resource. :
Contained By:	Springer Nature eBook
標題:	Information storage and retrieval. -
電子資源:	https://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. 2016. - XV, 526 p. 123 illus.online resource. - Undergraduate Topics in Computer Science,1863-7310. - Undergraduate Topics in Computer Science,.

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.

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:

1069252
Information storage and retrieval.

LC Class. No.: QA75.5-76.95

Dewey Class. No.: 025.04

Principles of Data Mining
LDR:03935nam a22004095i 4500 001 972183
003 DE-He213
005 20200705202044.0
007 cr nn 008mamaa
008 201211s2016 xxk| s |||| 0|eng d
020 $a 9781447173076 $9 978-1-4471-7307-6
024 7 $a 10.1007/978-1-4471-7307-6 $2 doi
035 $a 978-1-4471-7307-6
050 4 $a QA75.5-76.95
072 7 $a UNH $2 bicssc
072 7 $a COM030000 $2 bisacsh
072 7 $a UNH $2 thema
072 7 $a UND $2 thema
082 0 4 $a 025.04 $2 23
100 1 $a Bramer, Max. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 670035
245 1 0 $a Principles of Data Mining $h [electronic resource] / $c by Max Bramer.
250 $a 3rd ed. 2016.
264 1 $a London : $b Springer London : $b Imprint: Springer, $c 2016.
300 $a XV, 526 p. 123 illus. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
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: 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.
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 Information storage and retrieval. $3 1069252
650 0 $a Database management. $3 557799
650 0 $a Artificial intelligence. $3 559380
650 0 $a Computer programming. $3 527822
650 1 4 $a Information Storage and Retrieval. $3 593926
650 2 4 $a Database Management. $3 669820
650 2 4 $a Artificial Intelligence. $3 646849
650 2 4 $a Programming Techniques. $3 669781
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9781447173069
776 0 8 $i Printed edition: $z 9781447173083
830 0 $a Undergraduate Topics in Computer Science, $x 1863-7310 $3 1254738
856 4 0 $u https://doi.org/10.1007/978-1-4471-7307-6
912 $a ZDB-2-SCS
912 $a ZDB-2-SXCS
950 $a Computer Science (SpringerNature-11645)
950 $a Computer Science (R0) (SpringerNature-43710)