Business Intelligence Data Mining and Optimization for Decision Making

by Vercellis, Carlo

Edition: 1st

ISBN13: 9780470511381

ISBN10: 0470511389

Format: Hardcover

Pub. Date: 2009-04-20

Publisher(s): Wiley

Other versions by this Author

List Price: ~~$221.81~~

Buy New

Usually Ships in 8 - 10 Business Days.

$221.70

Add to Cart

Rent Textbook

Select for Price

Add to Cart

There was a problem. Please try again later.

Used Textbook

We're Sorry
Sold Out

eTextbook

We're Sorry
Not Available

Buy from our Marketplace starting at $136.73

Summary

Business intelligence is a broad category of applications and technologies for gathering, providing access to, and analyzing data for the purpose of helping enterprise users make better business decisions. This book provides coverage of topics currently dispersed throughout data mining and business books, bringing them topics together for the first time to provides readers with an introductory and practical guide to the mathematical models and analysis methodologies vital to business intelligence. It makes topics more accessible through the careful definition and introduction of each concept, followed by the extensive use of examples and numerous real-life case studies

Author Biography

Carlo Vercellis - School of Management, Politecnico di Milano, Italy

As well as teaching courses in Operations Research and Business Intelligence, Professor Vercellis is director of the research group MOLD (Mathematical Modeling, Optimization, Learning from Data). He has written four book in Italian, contributed to numerous other books, and has had many papers published in a variety of international journals.

Preface	p. xiii
Components of the decision-making process	p. 1
Business intelligence	p. 3
Effective and timely decisions	p. 3
Data, information and knowledge	p. 6
The role of mathematical models	p. 8
Business intelligence architectures	p. 9
Cycle of a business intelligence analysis	p. 11
Enabling factors in business intelligence projects	p. 13
Development of a business intelligence system	p. 14
Ethics and business intelligence	p. 17
Notes and readings	p. 18
Decision support systems	p. 21
Definition of system	p. 21
Representation of the decision-making process	p. 23
Rationality and problem solving	p. 24
The decision-making process	p. 25
Types of decisions	p. 29
Approaches to the decision-making process	p. 33
Evolution of information systems	p. 35
Definition of decision support system	p. 36
Development of a decision support system	p. 40
Notes and readings	p. 43
Data warehousing	p. 45
Definition of data warehouse	p. 45
Data marts	p. 49
Data quality	p. 50
Data warehouse architecture	p. 51
ETL tools	p. 53
Metadata	p. 54
Cubes and multidimensional analysis	p. 55
Hierarchies of concepts and OLAP operations	p. 60
Materialization of cubes of data	p. 61
Notes and readings	p. 62
Mathematical models and methods	p. 63
Mathematical models for decision making	p. 65
Structure of mathematical models	p. 65
Development of a model	p. 67
Classes of models	p. 70
Notes and readings	p. 75
Data mining	p. 77
Definition of data mining	p. 77
Models and methods for data mining	p. 79
Data mining, classical statistics and OLAP	p. 80
Applications of data mining	p. 81
Representation of input data	p. 82
Data mining process	p. 84
Analysis methodologies	p. 90
Notes and readings	p. 94
Data preparation	p. 95
Data validation	p. 95
Incomplete data	p. 96
Data affected by noise	p. 97
Data transformation	p. 99
Standardization	p. 99
Feature extraction	p. 100
Data reduction	p. 100
Sampling	p. 101
Feature selection	p. 102
Principal component analysis	p. 104
Data discretization	p. 109
Data exploration	p. 113
Univariate analysis	p. 113
Graphical analysis of categorical attributes	p. 114
Graphical analysis of numerical attributes	p. 116
Measures of central tendency for numerical attributes	p. 118
Measures of dispersion for numerical attributes	p. 121
Measures of relative location for numerical attributes	p. 126
Identification of outliers for numerical attributes	p. 127
Measures of heterogeneity for categorical attributes	p. 129
Analysis of the empirical density	p. 130
Summary statistics	p. 135
Bivariate analysis	p. 136
Graphical analysis	p. 136
Measures of correlation for numerical attributes	p. 142
Contingency tables for categorical attributes	p. 145
Multivariate analysis	p. 147
Graphical analysis	p. 147
Measures of correlation for numerical attributes	p. 149
Notes and readings	p. 152
Regression	p. 153
Structure of regression models	p. 153
Simple linear regression	p. 156
Calculating the regression line	p. 158
Multiple linear regression	p. 161
Calculating the regression coefficients	p. 162
Assumptions on the residuals	p. 163
Treatment of categorical predictive attributes	p. 166
Ridge regression	p. 167
Generalized linear regression	p. 168
Validation of regression models	p. 168
Normality and independence of the residuals	p. 169
Significance of the coefficients	p. 172
Analysis of variance	p. 174
Coefficient of determination	p. 175
Coefficient of linear correlation	p. 176
Multicollinearity of the independent variables	p. 177
Confidence and prediction limits	p. 178
Selection of predictive variables	p. 179
Example of development of a regression model	p. 180
Notes and readings	p. 185
Time series	p. 187
Definition of time series	p. 187
Index numbers	p. 190
Evaluating time series models	p. 192
Distortion measures	p. 192
Dispersion measures	p. 193
Tracking signal	p. 194
Analysis of the components of time series	p. 195
Moving average	p. 196
Decomposition of a time series	p. 198
Exponential smoothing models	p. 203
Simple exponential smoothing	p. 203
Exponential smoothing with trend adjustment	p. 204
Exponential smoothing with trend and seasonality	p. 206
Simple adaptive exponential smoothing	p. 207
Exponential smoothing with damped trend	p. 208
Initial values for exponential smoothing models	p. 209
Removal of trend and seasonality	p. 209
Autoregressive models	p. 210
Moving average models	p. 212
Autoregressive moving average models	p. 212
Autoregressive integrated moving average models	p. 212
Identification of autoregressive models	p. 213
Combination of predictive models	p. 216
The forecasting process	p. 217
Characteristics of the forecasting process	p. 217
Selection of a forecasting method	p. 219
Notes and readings	p. 219
Classification	p. 221
Classification problems	p. 221
Taxonomy of classification models	p. 224
Evaluation of classification models	p. 226
Holdout method	p. 228
Repeated random sampling	p. 228
Cross-validation	p. 229
Confusion matrices	p. 230
ROC curve charts	p. 233
Cumulative gain and lift charts	p. 234
Classification trees	p. 236
Splitting rules	p. 240
Univariate splitting criteria	p. 243
Example of development of a classification tree	p. 246
Stopping criteria and pruning rules	p. 250
Bayesian methods	p. 251
Naive Bayesian classifiers	p. 252
Example of naive Bayes classifier	p. 253
Bayesian networks	p. 256
Logistic regression	p. 257
Neural networks	p. 259
The Rosenblatt perceptron	p. 259
Multi-level feed-forward networks	p. 260
Support vector machines	p. 262
Structural risk minimization	p. 262
Maximal margin hyperplane for linear separation	p. 266
Nonlinear separation	p. 270
Notes and readings	p. 275
Association rules	p. 277
Motivation and structure of association rules	p. 277
Single-dimension association rules	p. 281
Apriori algorithm	p. 284
Generation of frequent itemsets	p. 284
Generation of strong rules	p. 285
General Association rules	p. 288
Notes and readings	p. 290
Clustering	p. 293
Clustering methods	p. 293
Taxonomy of clustering methods	p. 294
Affinity measures	p. 296
Partition methods	p. 302
K-means algorithm	p. 302
K-medoids algorithm	p. 305
Hierarchical methods	p. 307
Agglomerative hierarchical methods	p. 308
Divisive hierarchical methods	p. 310
Evaluation of clustering models	p. 312
Notes and readings	p. 315
Business intelligence applications	p. 317
Marketing models	p. 319
Relational marketing	p. 320
Motivations and objectives	p. 320
An environment for relational marketing analysis	p. 327
Lifetime value	p. 329
The effect of latency in predictive models	p. 332
Acquisition	p. 333
Retention	p. 334
Cross-selling and up-selling	p. 335
Market basket analysis	p. 335
Web mining	p. 336
Salesforce management	p. 338
Decision processes in salesforce management	p. 339
Models for salesforce management	p. 342
Response functions	p. 343
Sales territory design	p. 346
Calls and product presentations planning	p. 347
Business case studies	p. 352
Retention in telecommunications	p. 352
Acquisition in the automotive industry	p. 354
Cross-selling in the retail industry	p. 358
Notes and readings	p. 360
Logistic and production models	p. 361
Supply chain optimization	p. 362
Optimization models for logistics planning	p. 364
Tactical planning	p. 364
Extra capacity	p. 365
Multiple resources	p. 366
Backlogging	p. 366
Minimum lots and fixed costs	p. 369
Bill of materials	p. 370
Multiple plants	p. 371
Revenue management systems	p. 372
Decision processes in revenue management	p. 373
Business case studies	p. 376
Logistics planning in the food industry	p. 376
Logistics planning in the packaging industry	p. 383
Notes and readings	p. 384
Data envelopment analysis	p. 385
Efficiency measures	p. 386
Efficient frontier	p. 386
The CCR model	p. 390
Definition of target objectives	p. 392
Peer groups	p. 393
Identification of good operating practices	p. 394
Cross-efficiency analysis	p. 394
Virtual inputs and virtual outputs	p. 395
Weight restrictions	p. 396
Other models	p. 396
Notes and readings	p. 397
Software tools	p. 399
Dataset repositories	p. 401
References	p. 403
Index	p. 413
Table of Contents provided by Ingram. All Rights Reserved.