Data mining and statistics for decision making:
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| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Chichester
Wiley
2011
|
| Edition: | 1. publ. |
| Series: | Wiley series in computational statistics
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Item Description: | Includes bibliographical references and index |
| Physical Description: | XXIV, 689 S. graph. Darst. |
| ISBN: | 9780470688298 |
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MARC
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| 084 | |a MAT 624f |2 stub | ||
| 100 | 1 | |a Tufféry, Stéphane |e Verfasser |4 aut | |
| 240 | 1 | 0 | |a Data mining et statistique decisionnelle |
| 245 | 1 | 0 | |a Data mining and statistics for decision making |c Stéphane Tufféry. Transl. by Rod Riesco |
| 250 | |a 1. publ. | ||
| 264 | 1 | |a Chichester |b Wiley |c 2011 | |
| 300 | |a XXIV, 689 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a Wiley series in computational statistics | |
| 500 | |a Includes bibliographical references and index | ||
| 650 | 4 | |a Data mining | |
| 650 | 4 | |a Data mining / Statistical methods | |
| 650 | 0 | 7 | |a Data Mining |0 (DE-588)4428654-5 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Statistische Entscheidungstheorie |0 (DE-588)4077850-2 |2 gnd |9 rswk-swf |
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| 689 | 0 | 1 | |a Statistische Entscheidungstheorie |0 (DE-588)4077850-2 |D s |
| 689 | 0 | |5 DE-604 | |
| 776 | 0 | 8 | |i Erscheint auch als |n Online-Ausgabe |z 978-0-470-97917-4 |
| 776 | 0 | 8 | |i Erscheint auch als |n Online-Ausgabe, EPUB |z 978-0-470-97928-0 |
| 776 | 0 | 8 | |i Erscheint auch als |n Online-Ausgabe, PDF |z 978-0--470-97916-7 |
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Record in the Search Index
| _version_ | 1804143500672892929 |
|---|---|
| adam_text | Contents
Preface
xvii
Foreword
xxi
Foreword from the French language edition
xxiii
List of trademarks
xxv
1
Overview of data mining
1
1.1
What is data mining?
1
1.2
What is data mining used for?
4
1.2.1
Data mining in different sectors
4
1.2.2
Data mining in different applications
8
1.3
Data mining and statistics
11
1.4
Data mining and information technology
12
1.5
Data mining and protection of personal data
16
1.6
Implementation of data mining
23
2
The development of a data mining study
25
2.1
Defining the aims
26
2.2
Listing the existing data
26
2.3
Collecting the data
27
2.4
Exploring and preparing the data
30
2.5
Population segmentation
33
2.6
Drawing up and validating predictive models
35
2.7
Synthesizing predictive models of different segments
36
2.8
Iteration of the preceding steps
37
2.9
Deploying the models
37
2.10
Training the model users
38
2.11
Monitoring the models
38
2.12
Enriching the models
40
2.13
Remarks
41
2.14
Life cycle of a model
41
2.15
Costs of a pilot project
41
3
Data exploration and preparation
43
3.1
The different types of data
43
3.2
Examining the distribution of variables
44
3.3
Detection of rare or missing values
45
3.4
Detection of aberrant values
49
3.5
Detection of extreme values
52
viii CONTENTS
3.6
Tests of normality
52
3.7
Homoscedasticity and heteroscedasticity
58
3.8
Detection of the most discriminating variables
59
3.8.1
Qualitative, discrete or binned independent variables
60
3.8.2
Continuous independent variables
62
3.8.3
Details of single-factor non-parametric tests
65
3.8.4
ODS
and automated selection of discriminating
variables
70
3.9
Transformation of variables
73
3.10
Choosing ranges of values of binned variables
74
3.11
Creating new variables
81
3.12
Detecting interactions
82
3.13
Automatic variable selection
85
3.14
Detection of
collineari
ty
86
3.15
Sampling
89
3.15.1
Using sampling
89
3.15.2
Random sampling methods
90
4
Using commercial data
93
4.1
Data used in commercial applications
93
93
94
94
96
96
97
97
98
4.2
Special data
98
98
105
4.3
Data used by business sector
106
106
108
108
109
5
Statistical and data mining software 111
5.1
Types of data mining and statistical software
111
5.2
Essential characteristics of the software
114
5.2.1
Points of comparison
114
5.2.2
Methods implemented
115
5.2.3
Data preparation functions
116
5.2.4
Other functions
116
5.2.5
Technical characteristics
117
5.3
The main software packages
117
5.3.1
Overview
117
4.1.1
Data on transactions and RFM data
4.1.2
Data on products and contracts
4.1.3
Lifetimes
4.1.4
Data on channels
4.1.5
Relational, attitudinal and psychographic data
4.1.6
Sociodemographic data
4.1.7
When data are unavailable
4.1.8
Technical data
Special
data
4.2.1
Geodemographic data
4.2.2
Profitability
Data used by business sector
4.3.1
Data used in banking
4.3.2
Data used in insurance
4.3.3
Data used in telephony
4.3.4
Data used in mail order
CONTENTS ix
5.3.2 IBM
SPSS
119
5.3.3
SAS
122
5.3.4
R
124
5.3.5
Some elements of the
R
language
133
5.4
Comparison of R,
SAS
and IBM SPSS
136
5.5
How to reduce processing time
164
6
An outline of data mining methods
167
6.1
Classification of the methods
167
6.2
Comparison of the methods
174
7
Factor analysis
175
7.1
Principal component analysis
175
7.1.1
Introduction
175
7.1.2
Representation of variables
181
7.1.3
Representation of individuals
185
7.1.4
UseofPCA
187
7.1.5
Choosing the number of factor axes
189
7.1.6
Summary
192
7.2
Variants of principal component analysis
192
7.2.1
PCA with rotation
192
7.2.2
PCA of ranks
193
7.2.3
PCA on qualitative variables
194
7.3
Correspondence analysis
194
7.3.1
Introduction
194
7.3.2
Implementing CA with IBM SPSS Statistics
197
7.4
Multiple correspondence analysis
201
7.4.1
Introduction
201
7.4.2
Review of CA and MCA
205
7.4.3
Implementing MCA and CA with
SAS
207
8
Neural networks
217
8.1
General information on neural networks
217
8.2
Structure of a neural network
220
8.3
Choosing the learning sample
221
8.4
Some empirical rules for network design
222
8.5
Data normalization
223
8.5.1
Continuous variables
223
8.5.2
Discrete variables
223
8.5.3
Qualitative variables
224
8.6
Learning algorithms
224
8.7
The main neural networks
224
8.7.1
The multilayer perceptron
225
8.7.2
The radial basis function network
227
8.7.3
The Kohonen network
231
CONTENTS
Cluster analysis
235
9.1
Definition of clustering
235
9.2
Applications of clustering
236
9.3
Complexity of clustering
236
9.4
Clustering structures
237
9.4.1
Structure of the data to be clustered
237
9.4.2
Structure of the resulting clusters
237
9.5
Some methodological considerations
238
9.5.1
The optimum number of clusters
238
9.5.2
The use of certain types of variables
238
9.5.3
The use of illustrative variables
239
9.5.4
Evaluating the quality of clustering
239
9.5.5
Interpreting the resulting clusters
240
9.5.6
The criteria for correct clustering
242
9.6
Comparison of factor analysis and clustering
242
9.7
Within-cluster and between-cluster sum of squares
243
9.8
Measurements of clustering quality
244
9.8.1
All types of clustering
245
9.8.2
Agglomerative hierarchical clustering
246
9.9
Partitioning methods
247
9.9.1
The moving centres method
247
9.9.2
K-means and dynamic clouds
248
9.9.3
Processing qualitative data
249
9.9.4 Ä-medoids
and their variants
249
9.9.5
Advantages of the partitioning methods
250
9.9.6
Disadvantages of the partitioning methods
251
9.9.7
Sensitivity to the choice of initial centres
252
9.10
Agglomerative hierarchical clustering
253
9.10.1
Introduction
253
9.10.2
The main distances used
254
9.10.3
Density estimation methods
258
9.10.4
Advantages of agglomerative hierarchical clustering
259
9.10.5
Disadvantages of agglomerative hierarchical clustering
261
9.11
Hybrid clustering methods
261
9.11.1
Introduction
261
9.11.2
Illustration using
SAS
Software
262
9.12
Neural clustering
272
9.12.1
Advantages
272
9.12.2
Disadvantages
272
9.13
Clustering by similarity aggregation
273
9.13.1
Principle of relational analysis
273
9.13.2
Implementing clustering by similarity aggregation
274
9.13.3
Example of use of the
R
amap package
275
9.13.4
Advantages of clustering by similarity aggregation
277
9.13.5
Disadvantages of clustering by similarity aggregation
278
9.14
Clustering of numeric variables
278
9.15
Overview of clustering methods
286
CONTENTS xi
10
Association
analysis
287
10.1
Principles
287
10.2
Using taxonomy
291
10.3
Using supplementary variables
292
10.4
Applications
292
10.5
Example of use
294
11
Classification and prediction methods
301
11.1
Introduction
301
11.2
Inductive and transductive methods
302
11.3
Overview of classification and prediction methods
304
11.3.1
The qualities expected from a classification and prediction
method
304
11.3.2
Generalizability
305
11.3.3
Vápnik s
learning theory
308
11.3.4
Overfilling
310
11.4
Classification by decision tree
313
11.4.1
Principle of the decision trees
313
11.4.2
Definitions
-
the first step in creating the tree
313
11.4.3
Splitting criterion
316
11.4.4
Distribution among nodes
-
the second step in creating
the tree
318
11.4.5
Pruning
-
the third step in creating the tree
319
11.4.6
A pitfall to avoid
320
11.4.7
The CART, C5.0 and CHAID trees
321
11.4.8
Advantages of decision trees
327
11.4.9
Disadvantages of decision trees
328
11.5
Prediction by decision tree
330
11.6
Classification by discriminant analysis
332
11.6.1
The problem
332
11.6.2
Geometric descriptive discriminant analysis (discriminant
factor analysis)
333
11.6.3
Geometric predictive discriminant analysis
338
11.6.4
Probabilistic discriminant analysis
342
11.6.5
Measurements of the quality of the model
345
11.6.6
Syntax of discriminant analysis in
SAS
350
11.6.7
Discriminant analysis on qualitative variables
(DISQUAL Method)
352
11.6.8
Advantages of discriminant analysis
354
11.6.9
Disadvantages of discriminant analysis
354
11.7
Prediction by linear regression
355
11.7.1
Simple linear regression
356
11.7.2
Multiple linear regression and regularized regression
359
11.7.3
Tests in linear regression
365
11.7.4
Tests on residuals
371
11.7.5
The influence of observations
375
11.7.6
Example of linear regression
377
CONTENTS
11.7.7
Further details of the
SAS
linear regression syntax
383
11.7.8
Problems of collinearity in linear regression: an example
using
R
387
11.7.9
Problems of collinearity in linear regression:
diagnosis and solutions
394
11.7.10
PLS regression
397
11.7.11
Handling regularized regression with
SAS
and
R
400
11.7.12
Robust regression
430
11.7.13
The general linear model
434
11.8
Classification by logistic regression
437
11.8.1
Principles of binary logistic regression
437
11.8.2
Logit,
probit
and log-log logistic regressions
441
11.8.3
Odds ratios
443
11.8.4
Illustration of division into categories
445
11.8.5
Estimating the parameters
446
11.8.6
Deviance and quality measurement in a model
449
11.8.7
Complete separation in logistic regression
453
11.8.8
Statistical tests in logistic regression
454
11.8.9
Effect of division into categories and choice
of the reference category
458
11.8.10
Effect of collinearity
459
11.8.11
The effect of sampling on logit regression
460
11.8.12
The syntax of logistic regression in
SAS
Software
461
11.8.13
An example of modelling by logistic regression
463
11.8.14
Logistic regression with
R
474
11.8.15
Advantages of logistic regression
477
11.8.16
Advantages of the logit model compared with
probit
478
11.8.17
Disadvantages of logistic regression
478
11.9
Developments in logistic regression
479
11.9.1
Logistic regression on individuals with different weights
479
11.9.2
Logistic regression with correlated data
479
11.9.3
Ordinal logistic regression
482
11.9.4
Multinomial logistic regression
482
11.9.5
PLS logistic regression
483
11.9.6
The generalized linear model
484
11.9.7
Poisson
regression
487
11.9.8
The generalized additive model
491
11.10
Bayesian methods
492
11.10.1
The naive Bayesian classifier
492
11.10.2
Bayesian networks
497
11.11
Classification and prediction by neural networks
499
11.11.1
Advantages of neural networks
499
11.11.2
Disadvantages of neural networks
500
11.12
Classification by support vector machines
501
11.12.1
Introduction to SVMs
501
11.12.2
Example
506
11.12.3
Advantages of SVMs
508
11.12.4
Disadvantages of SVMs
508
CONTENTS xiii
11.13
Prediction by genetic algorithms
510
11.13.1
Random generation of initial rules
511
11.13.2
Selecting the best rules
512
11.13.3
Generating new rules
512
11.13.4
End of the algorithm
513
11.13.5
Applications of genetic algorithms
513
11.13.6
Disadvantages of genetic algorithms
514
11.14
Improving the performance of a predictive model
514
11.15
Bootstrapping and ensemble methods
516
11.15.1
Bootstrapping
516
11.15.2
Bagging
518
11.15.3
Boosting
521
11.15.4
Some applications
528
11.15.5
Conclusion
532
11.16
Using classification and prediction methods
534
11.16.1
Choosing the modelling methods
534
11.16.2
The training phase of a model
537
11.16.3
Reject inference
539
11.16.4
The test phase of a model
540
11.16.5
The ROC curve, the lift curve and the
Gini
index
542
11.16.6
The classification table of a model
551
11.16.7
The validation phase of a model
553
11.16.8
The application phase of a model
553
12
An application of data mining: scoring
555
12.1
The different types of score
555
12.2
Using propensity scores and risk scores
556
12.3
Methodology
558
12.3.1
Determining the objectives
558
12.3.2
Data inventory and preparation
559
12.3.3
Creating the analysis base
559
12.3.4
Developing a predictive model
561
12.3.5
Using the score
561
12.3.6
Deploying the score
562
12.3.7
Monitoring the available tools
562
12.4
Implementing a strategic score
562
12.5
Implementing an operational score
563
12.6
Scoring solutions used in a business
564
12.6.1
In-house or outsourced?
564
12.6.2
Generic or personalized score
567
12.6.3
Summary of the possible solutions
567
12.7
An example of credit scoring (data preparation)
567
12.8
An example of credit scoring (modelling by logistic regression)
594
12.9
An example of credit scoring (modelling by DISQUAL discriminant
analysis)
604
12.10
A brief history of credit scoring
615
References
616
xiv CONTENTS
13
Factors for success in a data mining project
617
13.1
The subject
617
13.2
The people
618
13.3
The data
618
13.4
The IT systems
619
13.5
The business culture
620
13.6
Data mining: eight common misconceptions
621
13.6.1
No a priori knowledge is needed
621
13.6.2
No specialist staff are needed
621
13.6.3
No statisticians are needed ( you can just press a button )
622
13.6.4
Data mining will reveal unbelievable wonders
622
13.6.5
Data mining is revolutionary
623
13.6.6
You must use all the available data
623
13.6.7
You must always sample
623
13.6.8
You must never sample
623
13.7
Return on investment
624
14
Text mining
627
14.1
Definition of text mining
627
14.2
Text sources used
629
14.3
Using text mining
629
14.4
Information retrieval
630
14.4.1
Linguistic analysis
630
14.4.2
Application of statistics and data mining
633
14.4.3
Suitable methods
633
14.5
Information extraction
635
14.5.1
Principles of information extraction
635
14.5.2
Example of application: transcription of business
interviews
635
14.6
Multi-type data mining
636
15
Web mining
637
15.1
The aims of web mining
637
15.2
Global analyses
638
15.2.1
What can they be used for?
638
15.2.2
The structure of the log file
638
15.2.3
Using the log file
639
15.3
Individual analyses
641
15.4
Personal analysis
642
Appendix A Elements of statistics
645
A.I A brief history
645
A.
1.1
A few dates
645
A.
1.2
From statistics
...
to data mining
645
A.2 Elements of statistics
648
A.2.1 Statistical characteristics
648
CONTENTS xv
A.2.2
Box and whisker plot
649
Α.
2.3
Hypothesis testing
649
Α.
2.4
Asymptotic, exact, parametric and non-parametric tests
652
A.2.5 Confidence interval for a mean: student s
t
test
652
A.
2.6
Confidence interval of a frequency (or proportion)
654
A.
2.7
The relationship between two continuous variables:
the linear correlation coefficient
656
A.
2.8
The relationship between two numeric or ordinal variables:
Spearman s rank correlation coefficient and Kendall s
tau 657
A.
2.9
The relationship between
n
sets of several continuous
or binary variables: canonical correlation analysis
658
A.2.10 The relationship between two nominal variables:
the
χ2
test
659
A.2.1
1
Example of use of the
χ2
test
660
A.
2.12
The relationship between two nominal variables:
Cramér s
coefficient
661
A.
2.13
The relationship between a nominal variable
and a numeric variable: the variance test
(one-way ANOVA test)
662
A.
2.14
The cox semi-parametric survival model
664
A.3 Statistical tables
665
A.
3.1
Table of the standard normal distribution
665
A.3.2 Table of student s
t
distribution
665
A.3.3 Chi-Square table
666
A.
3.4
Table of the Fisher-Snedecor distribution at the
0.05
significance level
667
A.3.5 Table of the Fisher-Snedecor distribution at the
0.10
significance level
673
Appendix
В
Further reading
675
B.I Statistics and data analysis
675
B.2 Data mining and statistical learning
678
B.3 Text mining
680
B.4 Web mining
680
B.5
R
software
680
B.6
SAS
software
681
B.7 IBM SPSS software
682
B.8 Websites
682
Index
685
|
| any_adam_object | 1 |
| author | Tufféry, Stéphane |
| author_facet | Tufféry, Stéphane |
| author_role | aut |
| author_sort | Tufféry, Stéphane |
| author_variant | s t st |
| building | Verbundindex |
| bvnumber | BV036806434 |
| classification_rvk | SK 830 ST 530 |
| classification_tum | MAT 624f |
| ctrlnum | (OCoLC)706018091 (DE-599)BVBBV036806434 |
| discipline | Informatik Mathematik |
| edition | 1. publ. |
| format | Book |
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| id | DE-604.BV036806434 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T22:48:39Z |
| institution | BVB |
| isbn | 9780470688298 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-020722507 |
| oclc_num | 706018091 |
| open_access_boolean | |
| owner | DE-473 DE-BY-UBG DE-824 DE-91G DE-BY-TUM DE-83 DE-1043 |
| owner_facet | DE-473 DE-BY-UBG DE-824 DE-91G DE-BY-TUM DE-83 DE-1043 |
| physical | XXIV, 689 S. graph. Darst. |
| publishDate | 2011 |
| publishDateSearch | 2011 |
| publishDateSort | 2011 |
| publisher | Wiley |
| record_format | marc |
| series2 | Wiley series in computational statistics |
| spelling | Tufféry, Stéphane Verfasser aut Data mining et statistique decisionnelle Data mining and statistics for decision making Stéphane Tufféry. Transl. by Rod Riesco 1. publ. Chichester Wiley 2011 XXIV, 689 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Wiley series in computational statistics Includes bibliographical references and index Data mining Data mining / Statistical methods Data Mining (DE-588)4428654-5 gnd rswk-swf Statistische Entscheidungstheorie (DE-588)4077850-2 gnd rswk-swf Data Mining (DE-588)4428654-5 s Statistische Entscheidungstheorie (DE-588)4077850-2 s DE-604 Erscheint auch als Online-Ausgabe 978-0-470-97917-4 Erscheint auch als Online-Ausgabe, EPUB 978-0-470-97928-0 Erscheint auch als Online-Ausgabe, PDF 978-0--470-97916-7 Digitalisierung UB Bamberg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020722507&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Tufféry, Stéphane Data mining and statistics for decision making Data mining Data mining / Statistical methods Data Mining (DE-588)4428654-5 gnd Statistische Entscheidungstheorie (DE-588)4077850-2 gnd |
| subject_GND | (DE-588)4428654-5 (DE-588)4077850-2 |
| title | Data mining and statistics for decision making |
| title_alt | Data mining et statistique decisionnelle |
| title_auth | Data mining and statistics for decision making |
| title_exact_search | Data mining and statistics for decision making |
| title_full | Data mining and statistics for decision making Stéphane Tufféry. Transl. by Rod Riesco |
| title_fullStr | Data mining and statistics for decision making Stéphane Tufféry. Transl. by Rod Riesco |
| title_full_unstemmed | Data mining and statistics for decision making Stéphane Tufféry. Transl. by Rod Riesco |
| title_short | Data mining and statistics for decision making |
| title_sort | data mining and statistics for decision making |
| topic | Data mining Data mining / Statistical methods Data Mining (DE-588)4428654-5 gnd Statistische Entscheidungstheorie (DE-588)4077850-2 gnd |
| topic_facet | Data mining Data mining / Statistical methods Data Mining Statistische Entscheidungstheorie |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020722507&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT tufferystephane dataminingetstatistiquedecisionnelle AT tufferystephane dataminingandstatisticsfordecisionmaking |