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Principles of Data Mining (Record no. 43683)

MARC details
000 -LEADER
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001 - CONTROL NUMBER
control field sulb-eb0021591
003 - CONTROL NUMBER IDENTIFIER
control field BD-SySUS
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20160413122150.0
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION
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008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 130220s2013 xxk| s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781447148845
-- 978-1-4471-4884-5
024 7# - OTHER STANDARD IDENTIFIER
Standard number or code 10.1007/978-1-4471-4884-5
Source of number or code doi
050 #4 - LIBRARY OF CONGRESS CALL NUMBER
Classification number QA75.5-76.95
072 #7 - SUBJECT CATEGORY CODE
Subject category code UNH
Source bicssc
Subject category code UND
Source bicssc
Subject category code COM030000
Source bisacsh
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 025.04
Edition number 23
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Bramer, Max.
Relator term author.
245 10 - TITLE STATEMENT
Title Principles of Data Mining
Medium [electronic resource] /
Statement of responsibility, etc. by Max Bramer.
250 ## - EDITION STATEMENT
Edition statement 2nd ed. 2013.
264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE
Place of production, publication, distribution, manufacture London :
Name of producer, publisher, distributor, manufacturer Springer London :
-- Imprint: Springer,
Date of production, publication, distribution, manufacture, or copyright notice 2013.
300 ## - PHYSICAL DESCRIPTION
Extent XIV, 440 p. 101 illus.
Other physical details online resource.
336 ## - CONTENT TYPE
Content type term text
Content type code txt
Source rdacontent
337 ## - MEDIA TYPE
Media type term computer
Media type code c
Source rdamedia
338 ## - CARRIER TYPE
Carrier type term online resource
Carrier type code cr
Source rdacarrier
347 ## - DIGITAL FILE CHARACTERISTICS
File type text file
Encoding format PDF
Source rda
490 1# - SERIES STATEMENT
Series statement Undergraduate Topics in Computer Science,
International Standard Serial Number 1863-7310
505 0# - FORMATTED CONTENTS NOTE
Formatted contents note 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.
520 ## - SUMMARY, ETC.
Summary, etc. 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.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Computer science.
Topical term or geographic name as entry element Computer programming.
Topical term or geographic name as entry element Database management.
Topical term or geographic name as entry element Information storage and retrieval.
Topical term or geographic name as entry element Artificial intelligence.
Topical term or geographic name as entry element Computer Science.
Topical term or geographic name as entry element Information Storage and Retrieval.
Topical term or geographic name as entry element Database Management.
Topical term or geographic name as entry element Artificial Intelligence (incl. Robotics).
Topical term or geographic name as entry element Programming Techniques.
710 2# - ADDED ENTRY--CORPORATE NAME
Corporate name or jurisdiction name as entry element SpringerLink (Online service)
773 0# - HOST ITEM ENTRY
Title Springer eBooks
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Relationship information Printed edition:
International Standard Book Number 9781447148838
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
Uniform title Undergraduate Topics in Computer Science,
International Standard Serial Number 1863-7310
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier <a href="http://dx.doi.org/10.1007/978-1-4471-4884-5">http://dx.doi.org/10.1007/978-1-4471-4884-5</a>
912 ## -
-- ZDB-2-SCS
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme
Koha item type

No items available.