Data mining and statistics for decision making / (Record no. 18443)

MARC details
000 -LEADER
fixed length control field 10363cam a2200865Ia 4500
001 - CONTROL NUMBER
control field ocn716215543
003 - CONTROL NUMBER IDENTIFIER
control field OCoLC
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20230823095432.0
006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS--GENERAL INFORMATION
fixed length control field m o d
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION
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008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 110428s2011 enka ob 001 0 eng d
040 ## - CATALOGING SOURCE
Original cataloging agency UIU
Language of cataloging eng
Description conventions pn
Transcribing agency UIU
Modifying agency YDXCP
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019 ## -
-- 711780360
-- 765144014
-- 769189252
-- 769849270
-- 771999468
-- 772397870
-- 816879070
-- 961503244
-- 961597673
-- 962613432
-- 962729284
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9780470979174
Qualifying information (electronic bk.)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 0470979178
Qualifying information (electronic bk.)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9780470979167
Qualifying information (electronic bk.)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 047097916X
Qualifying information (electronic bk.)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 1283373971
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9781283373975
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
Cancelled/invalid ISBN 9780470979280
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
Cancelled/invalid ISBN 0470979283
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
Cancelled/invalid ISBN 9780470688298
Qualifying information (hardback)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
Cancelled/invalid ISBN 0470688297
024 8# - OTHER STANDARD IDENTIFIER
Standard number or code 9786613373977
029 1# - (OCLC)
OCLC library identifier AU@
System control number 000051576698
029 1# - (OCLC)
OCLC library identifier CHNEW
System control number 000720557
029 1# - (OCLC)
OCLC library identifier DEBBG
System control number BV041908537
029 1# - (OCLC)
OCLC library identifier DEBSZ
System control number 372823890
029 1# - (OCLC)
OCLC library identifier DEBSZ
System control number 397155158
029 1# - (OCLC)
OCLC library identifier DEBSZ
System control number 431052387
029 1# - (OCLC)
OCLC library identifier DEBSZ
System control number 449264181
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OCLC library identifier DKDLA
System control number 820120-katalog:000556369
029 1# - (OCLC)
OCLC library identifier HEBIS
System control number 299832619
029 1# - (OCLC)
OCLC library identifier NZ1
System control number 13876188
029 1# - (OCLC)
OCLC library identifier NZ1
System control number 14257160
029 1# - (OCLC)
OCLC library identifier NZ1
System control number 15290871
029 1# - (OCLC)
OCLC library identifier DEBBG
System control number BV043393085
035 ## - SYSTEM CONTROL NUMBER
System control number (OCoLC)716215543
Canceled/invalid control number (OCoLC)711780360
-- (OCoLC)765144014
-- (OCoLC)769189252
-- (OCoLC)769849270
-- (OCoLC)771999468
-- (OCoLC)772397870
-- (OCoLC)816879070
-- (OCoLC)961503244
-- (OCoLC)961597673
-- (OCoLC)962613432
-- (OCoLC)962729284
037 ## - SOURCE OF ACQUISITION
Stock number 10.1002/9780470979174
Source of stock number/acquisition Wiley InterScience
Note http://www3.interscience.wiley.com
041 1# - LANGUAGE CODE
Language code of text/sound track or separate title eng
Language code of original and/or intermediate translations of text fre
050 #4 - LIBRARY OF CONGRESS CALL NUMBER
Classification number QA76.9.D343
Item number T84 2011
072 #7 - SUBJECT CATEGORY CODE
Subject category code COM
Subject category code subdivision 021030
Source bisacsh
072 #7 - SUBJECT CATEGORY CODE
Subject category code PBT
Source bicssc
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.3/12
Edition number 22
049 ## - LOCAL HOLDINGS (OCLC)
Holding library MAIN
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Tuffery, Stephane.
245 10 - TITLE STATEMENT
Title Data mining and statistics for decision making /
Statement of responsibility, etc Stephane Tuffery.
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication, distribution, etc Chichester, West Sussex ;
-- Hoboken, NJ. :
Name of publisher, distributor, etc Wiley,
Date of publication, distribution, etc 2011.
300 ## - PHYSICAL DESCRIPTION
Extent 1 online resource (xxiv, 689 pages) :
Other physical details illustrations.
336 ## -
-- text
-- txt
-- rdacontent
337 ## -
-- computer
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-- rdamedia
338 ## -
-- online resource
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-- rdacarrier
347 ## -
-- data file
-- rda
380 ## -
-- Bibliography
490 1# - SERIES STATEMENT
Series statement Wiley series in computational statistics
505 0# - FORMATTED CONTENTS NOTE
Formatted contents note Front Matter -- Overview of Data Mining -- The Development of a Data Mining Study -- Data Exploration and Preparation -- Using Commercial Data -- Statistical and Data Mining Software -- An Outline of Data Mining Methods -- Factor Analysis -- Neural Networks -- Cluster Analysis -- Association Analysis -- Classification and Prediction Methods -- An Application of Data Mining: Scoring -- Factors for Success in a Data Mining Project -- Text Mining -- Web Mining -- Appendix A: Elements of Statistics -- Appendix B: Further Reading -- Index.
520 ## - SUMMARY, ETC.
Summary, etc "This practical guide to understanding and implementing data mining techniques discusses traditional methods--cluster analysis, factor analysis, linear regression, PLS regression, and generalized linear models--and recent methods--bagging and boosting, decision trees, neural networks, support vector machines, and genetic algorithm. The book focuses largely on credit scoring, one of the most common applications of predictive techniques, but also includes other descriptive techniques, such as customer segmentation. It also covers data mining with R, provides a comparison of SAS and SPSS, and includes an appendix presenting the necessary statistical background"--
-- Provided by publisher.
520 ## - SUMMARY, ETC.
Summary, etc "Data Mining is a practical guide to understanding and implementing data mining techniques, featuring traditional methods such as cluster analysis, factor analysis, linear regression, PLS regression and generalised linear models"--
-- Provided by publisher.
504 ## - BIBLIOGRAPHY, ETC. NOTE
Bibliography, etc Includes bibliographical references and index.
505 8# - FORMATTED CONTENTS NOTE
Formatted contents note Machine generated contents note: Preface -- Foreword -- Contents -- Overview of data mining -- 1.1. What is data mining? -- 1.2. What is data mining used for? -- 1.3. Data Mining and statistics -- 1.4. Data mining and information technology -- 1.5. Data mining and protection of personal data -- 1.6. Implementation of data mining -- The development of a data mining study -- 2.1. Defining the aims -- 2.2. Listing the existing data -- 2.3. Collecting the data -- 2.4. Exploring and preparing the data -- 2.5. Population segmentation -- 2.6. Drawing up and validating predictive models -- 2.7. Synthesizing predictive models of different segments -- 2.8. Iteration of the preceding steps -- 2.9. Deploying the models -- 2.10. Training the model users -- 2.11. Monitoring the models -- 2.12. Enriching the models -- 2.13. Remarks -- 2.14. Life cycle of a model -- 2.15. Costs of a pilot project -- Data exploration and preparation -- 3.1. The different types of data -- 3.2. Examining the distribution of variables -- 3.3. Detection of rare or missing values -- 3.4. Detection of aberrant values -- 3.5. Detection of extreme values -- 3.6. Tests of normality -- 3.7. Homoscedasticity and heteroscedasticity -- 3.8. Detection of the most discriminating variables -- 3.9. Transformation of variables -- 3.10. Choosing ranges of values of continuous variables -- 3.11. Creating new variables -- 3.12. Detecting interactions 89 -- 3.13. Automatic variable selection -- 3.14. Detection of collinearity -- 3.15. Sampling -- Using commercial data -- 4.1. Data used in commercial applications -- 4.2. Special data -- 4.3. Data used by business sector -- Statistical and data mining software -- 5.1. Types of data mining and statistical software -- 5.2. Essential characteristics of the software -- 5.3. The main software packages -- 5.4. Comparison of R, SAS and IBM SPSS -- 5.5. How to reduce processing time -- An outline of data mining methods -- 6.1. A note on terminology -- 6.2. Classification of the methods -- 6.3. Comparison of the methods -- 6.4. Using these methods in the business world -- Factor analysis -- 7.1. Principal component analysis -- 7.2. Variants of principal component analysis -- 7.3. Correspondence analysis -- 7.4. Multiple correspondence analysis -- Neural networks -- 8.1. General information on neural networks -- 8.2. Structure of a neural network -- 8.3. Choosing the training sample -- 8.4. Some empirical rules for network design -- 8.5. Data normalization -- 8.6. Learning algorithms -- 8.7. The main neural networks -- Automatic clustering methods -- 9.1. Definition of clustering -- 9.2. Applications of clustering -- 9.3. Complexity of clustering -- 9.4. Clustering structures -- 9.5. Some methodological considerations -- 9.6. Comparison of factor analysis and clustering -- 9.7. Intra-class and inter-class inertias -- 9.8. Measurements of clustering quality -- 9.9. Partitioning methods -- 9.10. Hierarchical ascending clustering -- 9.11. Hybrid clustering methods -- 9.12. Neural clustering -- 9.13. Clustering by aggregation of similarities -- 9.14. Clustering of numeric variables -- 9.15. Overview of clustering methods -- Finding associations -- 10.1. Principles -- 10.2. Using taxonomy -- 10.3. Using supplementary variables -- 10.4. Applications -- 10.5. Example of use -- Classification and prediction methods -- 11.1. Introduction -- 11.2. Inductive and transductive methods -- 11.3. Overview of classification and prediction methods -- 11.4. Classification by decision tree -- 11.5. Prediction by decision tree -- 11.6. Classification by discriminant analysis -- 11.7. Prediction by linear regression -- 11.8. Classification by logistic regression -- 11.9. Developments in logistic regression -- 11.10. Bayesian methods -- 11.11. Classification and prediction by neural networks -- 11.12. Classification by support vector machines (SVMs) -- 11.13. Prediction by genetic algorithms -- 11.14. Improving the performance of a predictive model -- 11.15. Bootstrapping and aggregation of models -- 11.16. Using classification and prediction methods -- An application of data mining: scoring -- 12.1. The different types of score -- 12.2. Using propensity scores and risk scores -- 12.3. Methodology -- 12.4. Implementing a strategic score -- 12.5. Implementing an operational score -- 12.6. The kinds of scoring solutions used in a business -- 12.7. An example of credit scoring (data preparation) -- 12.8. An example of credit scoring (modelling by logistic regression) -- 12.9. An example of credit scoring (modelling by DISQUAL discriminant analysis) -- 12.10. A brief history of credit scoring -- Factors for success in a data mining project -- 13.1. The subject -- 13.2. The people -- 13.3. The data -- 13.4. The IT systems -- 13.5. The business culture -- 13.6. Data mining: eight common misconceptions -- 13.7. Return on investment -- Text mining -- 14.1. Definition of text mining -- 14.2. Text sources used -- 14.3. Using text mining -- 14.4. Information retrieval -- 14.5. Information extraction -- 14.6. Multi-type data mining -- Web mining -- 15.1. The aims of web mining -- 15.2. Global analyses -- 15.3. Individual analyses -- 15.4. Personal analyses -- Appendix: Elements of statistics -- 16.1. A brief history -- 16.2. Elements of statistics -- 16.3. Statistical tables -- Further reading -- 17.1. Statistics and data analysis -- 17.2. Data mining and statistical learning -- 17.3. Text mining -- 17.4. Web mining -- 17.5. R software -- 17.6. SAS software -- 17.7. IBM SPSS software -- 17.8. Websites -- Index.
588 0# -
-- Print version record.
526 ## - STUDY PROGRAM INFORMATION NOTE
Department Management Information Systems
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Data mining.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Statistical decision.
650 #4 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Data mining.
650 #4 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Statistical decision.
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element COMPUTERS
General subdivision Database Management
-- Data Mining.
Source of heading or term bisacsh
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Data mining.
Source of heading or term fast
-- (OCoLC)fst00887946
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Statistical decision.
Source of heading or term fast
-- (OCoLC)fst01132059
655 #4 - INDEX TERM--GENRE/FORM
Genre/form data or focus term Electronic books.
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Display text Print version:
Main entry heading Tuffery, Stephane.
Title Data mining and statistics for decision making.
Place, publisher, and date of publication Chichester, West Sussex ; Hoboken, NJ. : Wiley, 2011
International Standard Book Number 9780470688298
Record control number (DLC) 2010039789
-- (OCoLC)669160723
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
Uniform title Wiley series in computational statistics.
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier <a href="http://dx.doi.org/10.1002/9780470979174">http://dx.doi.org/10.1002/9780470979174</a>
Public note Wiley Online Library
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-- DG1

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