Applied data mining for business and industry:
Gespeichert in:
| Vorheriger Titel: | Giudici, Paolo Applied data mining |
|---|---|
| Hauptverfasser: | , |
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Chichester
Wiley
2009
|
| Ausgabe: | 2. ed. |
| Schlagworte: | |
| Online-Zugang: | Klappentext Inhaltsverzeichnis |
| Beschreibung: | VIII, 249 S. graph. Darst. |
| ISBN: | 9780470058879 9780470058862 0470058862 |
Internformat
MARC
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Datensatz im Suchindex
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| adam_text | applied data mining
for business and industry
SECOND EDITION
Paolo Giudici
and Silvia Figini university of
Pavía,
Italy
The increasing availability of data in our current, information overloaded society
has led to the need for valid tools for its modelling and analysis. Data mining
and applied statistical methods are the appropriate tools to extract knowledge
from such data. This book provides an accessible introduction to data mining
methods in a consistent and application oriented statistical framework, using
case studies drawn from real industry projects and highlighting the use of data
mining methods in a variety of business applications.
•
Introduces data mining methods and applications.
•
Covers classical and Bayesian multivariate statistical methodology as
well as machine learning and computational data mining methods.
•
Includes many recent developments such as association and sequence
rules, graphical Markov models, lifetime value modelling, credit risk,
operational risk and web mining.
•
Features detailed case studies based on applied projects within industry.
•
Incorporates discussion of data mining software, with case studies
analysed using R.
•
Is accessible to anyone with a basic knowledge of statistics or data
analysis.
•
Includes an extensive bibliography and pointers to further reading
within the text.
Applied Data Mining for Business and Industry,
2nd
edition is aimed at advanced
undergraduate and graduate students of data mining, applied statistics, database
management, computer science and economics. The case studies will provide
guidance to professionals working in industry on projects involving large volumes of
data, such as customer relationship management, web design, risk management,
marketing, economics and finance.
Titel: Applied data mining for business and industry
Autor: Giudici, Paolo
Jahr: 2009
Contents
1 Introduction 1
Part I Methodology 5
2 Organisation of the data 7
2.1 Statistical units and statistical variables 7
2.2 Data matrices and their transformations 9
2.3 Complex data structures 10
2.4 Summary 11
3 Summary statistics 13
3.1 Univariate exploratory analysis 13
3.1.1 Measures of location 13
3.1.2 Measures of variability 15
3.1.3 Measures of heterogeneity 16
3.1.4 Measures of concentration 17
3.1.5 Measures of asymmetry 19
3.1.6 Measures of kurtosis 20
3.2 Bivariate exploratory analysis of quantitative data 22
3.3 Multivariate exploratory analysis of quantitative data 25
3.4 Multivariate exploratory analysis of qualitative data 27
3.4.1 Independence and association 28
3.4.2 Distance measures 29
3.4.3 Dependency measures 31
3.4.4 Model-based measures 32
3.5 Reduction of dimensionality 34
3.5.1 Interpretation of the principal components 36
3.6 Further reading 39
4 Model specification 41
4.1 Measures of distance 42
4.1.1 Euclidean distance 43
4.1.2 Similarity measures 44
4.1.3 Multidimensional scaling 46
vi CONTENTS
4.2 Cluster analysis 47
4.2.1 Hierarchical methods 49
4.2.2 Evaluation of hierarchical methods 53
4.2.3 Non-hierarchical methods 55
4.3 Linear regression 57
4.3.1 Bivariate linear regression 57
4.3.2 Properties of the residuals 60
4.3.3 Goodness of fit 62
4.3.4 Multiple linear regression 63
4.4 Logistic regression 67
4.4.1 Interpretation of logistic regression 68
4.4.2 Discriminant analysis 70
4.5 Tree models 71
4.5.1 Division criteria 73
4.5.2 Pruning 74
4.6 Neural networks 76
4.6.1 Architecture of a neural network 79
4.6.2 The multilayer perceptron 81
4.6.3 Kohonen networks 87
4.7 Nearest-neighbour models 89
4.8 Local models 90
4.8.1 Association rules 90
4.8.2 Retrieval by content 96
4.9 Uncertainty measures and inference 96
4.9.1 Probability 97
4.9.2 Statistical models 99
4.9.3 Statistical inference 103
4.10 Non-parametric modelling 109
4.11 The normal linear model H2
4.11.1 Main inferential results 113
4.12 Generalised linear models H6
4.12.1 The exponential family H7
4.12.2 Definition of generalised linear models 118
4.12.3 The logistic regression model 125
4.13 Log-linear models 126
4.13.1 Construction of a log-linear model 126
4.13.2 Interpretation of a log-linear model 128
4.13.3 Graphical log-linear models 129
4.13.4 Log-linear model comparison 132
4.14 Graphical models 133
4.14.1 Symmetric graphical models 135
4.14.2 Recursive graphical models 139
4.14.3 Graphical models and neural networks 141
4.15 Survival analysis models 142
4.16 Further reading 144
CONTENTS vii
5 Model evaluation 147
5.1 Criteria based on statistical tests 148
5.1.1 Distance between statistical models 148
5.1.2 Discrepancy of a statistical model 150
5.1.3 Kullback-Leibler discrepancy 151
5.2 Criteria based on scoring functions 153
5.3 Bayesian criteria 155
5.4 Computational criteria 156
5.5 Criteria based on loss functions 159
5.6 Further reading 162
Part II Business case studies 163
6 Describing website visitors 165
6.1 Objectives of the analysis 165
6.2 Description of the data 165
6.3 Exploratory analysis 167
6.4 Model building 167
6.4.1 Cluster analysis 168
6.4.2 Kohonen networks 169
6.5 Model comparison 171
6.6 Summary report 172
7 Market basket analysis 175
7.1 Objectives of the analysis 175
7.2 Description of the data 176
7.3 Exploratory data analysis 178
7.4 Model building 181
7.4.1 Log-linear models 181
7.4.2 Association rules 184
7.5 Model comparison 186
7.6 Summary report 191
8 Describing customer satisfaction 193
8.1 Objectives of the analysis 193
8.2 Description of the data 194
8.3 Exploratory data analysis 194
8.4 Model building 197
8.5 Summary 201
9 Predicting credit risk of small businesses 203
9.1 Objectives of the analysis 203
9.2 Description of the data 203
9.3 Exploratory data analysis 205
9.4 Model building 206
Vlll
CONTENTS
9.5 Model comparison
9.6 Summary report
Predicting e-learning student performance
10.1 Objectives of the analysis
10.2 Description of the data
10.3 Exploratory data analysis
10.4 Model specification
10.5 Model comparison
10.6 Summary report
209
210
10 Predicting e-learning student performance 211
211
212
212
214
217
218
11 Predicting customer lifetime value 219
11.1 Objectives of the analysis 219
11.2 Description of the data 220
11.3 Exploratory data analysis 221
11.4 Model specification 223
11.5 Model comparison 224
11.6 Summary report 225
12 Operational risk management 227
12.1 Context and objectives of the analysis 227
12.2 Exploratory data analysis 228
12.3 Model building 230
12.4 Model comparison 232
12.5 Summary conclusions 235
References 237
Index 243
|
| any_adam_object | 1 |
| author | Giudici, Paolo 1965- Figini, Silvia |
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| classification_rvk | QH 500 ST 530 |
| ctrlnum | (OCoLC)311075482 (DE-599)HBZHT015909891 |
| dewey-full | 005.74068 |
| dewey-hundreds | 000 - Computer science, information, general works |
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| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik Wirtschaftswissenschaften |
| edition | 2. ed. |
| format | Book |
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| spelling | Giudici, Paolo 1965- Verfasser (DE-588)134179196 aut Applied data mining for business and industry Paolo Giudici ; Silvia Figini 2. ed. Chichester Wiley 2009 VIII, 249 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Datenverarbeitung Wirtschaft Business Data processing Commercial statistics Data mining Wissensextraktion (DE-588)4546354-2 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf Betriebliches Informationssystem (DE-588)4069386-7 gnd rswk-swf Data Mining (DE-588)4428654-5 gnd rswk-swf Data Mining (DE-588)4428654-5 s Statistik (DE-588)4056995-0 s DE-604 Wissensextraktion (DE-588)4546354-2 s Betriebliches Informationssystem (DE-588)4069386-7 s Figini, Silvia Verfasser aut 1. Auflage Giudici, Paolo Applied data mining Digitalisierung UB Bayreuth application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017559052&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Klappentext HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017559052&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Giudici, Paolo 1965- Figini, Silvia Applied data mining for business and industry Datenverarbeitung Wirtschaft Business Data processing Commercial statistics Data mining Wissensextraktion (DE-588)4546354-2 gnd Statistik (DE-588)4056995-0 gnd Betriebliches Informationssystem (DE-588)4069386-7 gnd Data Mining (DE-588)4428654-5 gnd |
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| title | Applied data mining for business and industry |
| title_auth | Applied data mining for business and industry |
| title_exact_search | Applied data mining for business and industry |
| title_full | Applied data mining for business and industry Paolo Giudici ; Silvia Figini |
| title_fullStr | Applied data mining for business and industry Paolo Giudici ; Silvia Figini |
| title_full_unstemmed | Applied data mining for business and industry Paolo Giudici ; Silvia Figini |
| title_old | Giudici, Paolo Applied data mining |
| title_short | Applied data mining for business and industry |
| title_sort | applied data mining for business and industry |
| topic | Datenverarbeitung Wirtschaft Business Data processing Commercial statistics Data mining Wissensextraktion (DE-588)4546354-2 gnd Statistik (DE-588)4056995-0 gnd Betriebliches Informationssystem (DE-588)4069386-7 gnd Data Mining (DE-588)4428654-5 gnd |
| topic_facet | Datenverarbeitung Wirtschaft Business Data processing Commercial statistics Data mining Wissensextraktion Statistik Betriebliches Informationssystem Data Mining |
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