Business Intelligence Data Mining and Optimization for Decision Making
作者:Carlo Vercellis
出版:2009

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I Components of the decision-making process 1
1 Business intelligence 3
1.1 Effective and timely decisions . . . . . . . . . . . . . . . . . . 3
1.2 Data, information and knowledge . . . . . . . . . . . . . . . . 6
1.3 The role of mathematical models . . . . . . . . . . . . . . . . 8
1.4 Business intelligence architectures . . . . . . . . . . . . . . . . 9
1.5 Ethics and business intelligence . . . . . . . . . . . . . . . . . 17
1.6 Notes and readings . . . . . . . . . . . . . . . . . . . . . . . . 18
2 Decision support systems 21
2.1 Definition of system . . . . . . . . . . . . . . . . . . . . . . . 21
2.2 Representation of the decision-making process . . . . . . . . . 23
2.3 Evolution of information systems . . . . . . . . . . . . . . . . 35
2.4 Definition of decision support system . . . . . . . . . . . . . . 36
2.5 Development of a decision support system . . . . . . . . . . . 40
2.6 Notes and readings . . . . . . . . . . . . . . . . . . . . . . . . 43
3 Data warehousing 45
3.1 Definition of data warehouse . . . . . . . . . . . . . . . . . . . 45
3.2 Data warehouse architecture . . . . . . . . . . . . . . . . . . . 51
3.3 Cubes and multidimensional analysis . . . . . . . . . . . . . . 55
3.4 Notes and readings . . . . . . . . . . . . . . . . . . . . . . . . 62
II Mathematical models and methods 63
4 Mathematical models for decision making 65
4.1 Structure of mathematical models . . . . . . . . . . . . . . . . 65
4.2 Development of a model . . . . . . . . . . . . . . . . . . . . . 67
4.3 Classes of models . . . . . . . . . . . . . . . . . . . . . . . . . 70
4.4 Notes and readings . . . . . . . . . . . . . . . . . . . . . . . . 75
5 Data mining 77
5.1 Definition of data mining . . . . . . . . . . . . . . . . . . . . . 77
5.2 Representation of input data . . . . . . . . . . . . . . . . . . . 82
5.3 Data mining process . . . . . . . . . . . . . . . . . . . . . . . 84
5.4 Analysis methodologies . . . . . . . . . . . . . . . . . . . . . 90
5.5 Notes and readings . . . . . . . . . . . . . . . . . . . . . . . . 94
6 Data preparation 95
6.1 Data validation . . . . . . . . . . . . . . . . . . . . . . . . . . 95
6.2 Data transformation . . . . . . . . . . . . . . . . . . . . . . . . 99
6.3 Data reduction . . . . . . . . . . . . . . . . . . . . . . . . . . 100
7 Data exploration 113
7.1 Univariate analysis . . . . . . . . . . . . . . . . . . . . . . . . 113
7.2 Bivariate analysis . . . . . . . . . . . . . . . . . . . . . . . . . 136
7.3 Multivariate analysis . . . . . . . . . . . . . . . . . . . . . . . 147
7.4 Notes and readings . . . . . . . . . . . . . . . . . . . . . . . . 152
8 Regression 153
8.1 Structure of regression models . . . . . . . . . . . . . . . . . . 153
8.2 Simple linear regression . . . . . . . . . . . . . . . . . . . . . 156
8.3 Multiple linear regression . . . . . . . . . . . . . . . . . . . . 161
8.4 Validation of regression models . . . . . . . . . . . . . . . . . 168
8.5 Selection of predictive variables . . . . . . . . . . . . . . . . . 179
8.5.1 Example of development of a regression model . . . . 180
8.6 Notes and readings . . . . . . . . . . . . . . . . . . . . . . . . 185
9 Time series 187
9.1 Definition of time series . . . . . . . . . . . . . . . . . . . . . 187
9.1.1 Index numbers . . . . . . . . . . . . . . . . . . . . . . 190
9.2 Evaluating time series models . . . . . . . . . . . . . . . . . . 192
9.3 Analysis of the components of time series . . . . . . . . . . . 195
9.4 Exponential smoothing models . . . . . . . . . . . . . . . . . . 203
9.5 Autoregressive models . . . . . . . . . . . . . . . . . . . . . . 210
9.6 Combination of predictive models . . . . . . . . . . . . . . . . 216
9.7 The forecasting process . . . . . . . . . . . . . . . . . . . . . . 217
9.8 Notes and readings . . . . . . . . . . . . . . . . . . . . . . . . 219
10 Classification 221
10.1 Classification problems . . . . . . . . . . . . . . . . . . . . . . 221
10.1.1 Taxonomy of classification models . . . . . . . . . . . 224
10.2 Evaluation of classification models . . . . . . . . . . . . . . . 226
10.3 Classification trees . . . . . . . . . . . . . . . . . . . . . . . . 236
10.4 Bayesian methods . . . . . . . . . . . . . . . . . . . . . . . . . 251
10.5 Logistic regression . . . . . . . . . . . . . . . . . . . . . . . . 257
10.6 Neural networks . . . . . . . . . . . . . . . . . . . . . . . . . 259
10.7 Support vector machines . . . . . . . . . . . . . . . . . . . . . 262
10.8 Notes and readings . . . . . . . . . . . . . . . . . . . . . . . . 275
11 Association rules 277
11.1 Motivation and structure of association rules . . . . . . . . . . 277
11.2 Single-dimension association rules . . . . . . . . . . . . . . . . 281
11.3 Apriori algorithm . . . . . . . . . . . . . . . . . . . . . . . . . 284
11.4 General association rules . . . . . . . . . . . . . . . . . . . . . 288
11.5 Notes and readings . . . . . . . . . . . . . . . . . . . . . . . . 290
12 Clustering 293
12.1 Clustering methods . . . . . . . . . . . . . . . . . . . . . . . . 293
12.2 Partition methods . . . . . . . . . . . . . . . . . . . . . . . . . 302
12.3 Hierarchical methods . . . . . . . . . . . . . . . . . . . . . . . 307
12.4 Evaluation of clustering models . . . . . . . . . . . . . . . . . 312
12.5 Notes and readings . . . . . . . . . . . . . . . . . . . . . . . . 315
III Business intelligence applications 317
13 Marketing models 319
13.1 Relational marketing . . . . . . . . . . . . . . . . . . . . . . . 320
13.2 Salesforce management . . . . . . . . . . . . . . . . . . . . . . 338
13.3 Business case studies . . . . . . . . . . . . . . . . . . . . . . . 352
13.4 Notes and readings . . . . . . . . . . . . . . . . . . . . . . . . 360
14 Logistic and production models 361
14.1 Supply chain optimization . . . . . . . . . . . . . . . . . . . . 362
14.2 Optimization models for logistics planning . . . . . . . . . . . 364
14.3 Revenue management systems . . . . . . . . . . . . . . . . . . 372
14.3.1 Decision processes in revenue management . . . . . . 373
14.4 Business case studies . . . . . . . . . . . . . . . . . . . . . . . 376
14.5 Notes and readings . . . . . . . . . . . . . . . . . . . . . . . . 384
15 Data envelopment analysis 385
15.1 Efficiency measures . . . . . . . . . . . . . . . . . . . . . . . . 386
15.2 Efficient frontier . . . . . . . . . . . . . . . . . . . . . . . . . 386
15.3 The CCR model . . . . . . . . . . . . . . . . . . . . . . . . . 390
15.3.2 Peer groups . . . . . . . . . . . . . . . . . . . . . . . . 393
15.4 Identification of good operating practices . . . . . . . . . . . . 394
15.5 Other models . . . . . . . . . . . . . . . . . . . . . . . . . . . 396
15.6 Notes and readings . . . . . . . . . . . . . . . . . . . . . . . . 397
Appendix A Software tools 399
Appendix B Dataset repositories 401
References 403
Index 413