Business Intelligence
Data Mining and Optimization for Decision Making
Erschienen am 10. August 2011
436 Seiten
978-1-119-96547-3 (ISBN)
Beschreibung
Business intelligence is a broad category of applications andtechnologies for gathering, providing access to, and analyzing datafor the purpose of helping enterprise users make better businessdecisions. The term implies having a comprehensive knowledge of allfactors that affect a business, such as customers, competitors,business partners, economic environment, and internal operations,therefore enabling optimal decisions to be made.
Business Intelligence provides readers with anintroduction and practical guide to the mathematical models andanalysis methodologies vital to business intelligence.
This book:
* Combines detailed coverage with a practical guide to themathematical models and analysis methodologies of businessintelligence.
* Covers all the hot topics such as data warehousing, data miningand its applications, machine learning, classification, supplyoptimization models, decision support systems, and analyticalmethods for performance evaluation.
* Is made accessible to readers through the careful definitionand introduction of each concept, followed by the extensive use ofexamples and numerous real-life case studies.
* Explains how to utilise mathematical models and analysis modelsto make effective and good quality business decisions.
This book is aimed at postgraduate students following dataanalysis and data mining courses.
Researchers looking for a systematic and broad coverage oftopics in operations research and mathematical models fordecision-making will find this an invaluable guide.
Business Intelligence provides readers with anintroduction and practical guide to the mathematical models andanalysis methodologies vital to business intelligence.
This book:
* Combines detailed coverage with a practical guide to themathematical models and analysis methodologies of businessintelligence.
* Covers all the hot topics such as data warehousing, data miningand its applications, machine learning, classification, supplyoptimization models, decision support systems, and analyticalmethods for performance evaluation.
* Is made accessible to readers through the careful definitionand introduction of each concept, followed by the extensive use ofexamples and numerous real-life case studies.
* Explains how to utilise mathematical models and analysis modelsto make effective and good quality business decisions.
This book is aimed at postgraduate students following dataanalysis and data mining courses.
Researchers looking for a systematic and broad coverage oftopics in operations research and mathematical models fordecision-making will find this an invaluable guide.
Weitere Details
Weitere Ausgaben
Person
Carlo Vercellis - School of Management, Politecnico di Milano,Italy
As well as teaching courses in Operations Research and BusinessIntelligence, Professor Vercellis is director of the research groupMOLD (Mathematical Modeling, Optimization, Learning from Data). Hehas written four book in Italian, contributed to numerous otherbooks, and has had many papers published in a variety ofinternational journals.
As well as teaching courses in Operations Research and BusinessIntelligence, Professor Vercellis is director of the research groupMOLD (Mathematical Modeling, Optimization, Learning from Data). Hehas written four book in Italian, contributed to numerous otherbooks, and has had many papers published in a variety ofinternational journals.
Inhalt
Preface.
I. COMPONENTS OF THE DECISION MAKING PROCESS.
1. Business intelligence.
1.1 Effective and timely decisions.
1.2 Data, information and knowledge.
1.3 The role of mathematical models.
1.4 Business intelligence architectures.
1.5 Ethics and business intelligence.
1.6 Notes and readings.
2. Decision support systems.
2.1 Definition of system.
2.2 Representation of the decision making process.
2.3 Evolution of information.
2.4 Definition of decision support system.
2.5 Development of a decision support system.
2.6 Notes and readings.
3. Data warehousing.
3.1 Definition of data warehouse.
3.2 Data warehouse architecture.
3.3 Cubes and multidimensional analysis.
3.4 Notes and readings.
II. MATHEMATICAL MODELS AND METHODS.
4. Mathematical models for decision making.
4.1 Structure of mathematical models.
4.2 Development of a model.
4.3 Classes of models.
4.4 Notes and readings.
5. Data mining.
5.1 Definition of data mining.
5.2 Representation of input data.
5.3 Data mining process.
5.4 Analysis methodologies.
5.5 Notes and readings.
6. Data preparation.
6.1 Data validation.
6.2 Data transformation.
6.3 Data reduction.
7. Data exploration.
7.1 Univariate analysis.
7.2 Bivariate analysis.
7.3 Multivariate analysis.
7.4 Notes and readings.
8. Regression.
8.1 Structure of regression models.
8.2 Simple linear regression.
8.3 Multiple linear regression.
8.4 Validation of regression models.
8.5 Selection of predictive variables.
8.6 Notes and readings.
9. Time series.
9.1 Definition of time series.
9.2 Evaluating time series models.
9.3 Analysis of the components of time series.
9.4 Exponential smoothing models.
9.5 Autoregressive models.
9.6 Combination of predictive models.
9.7 The forecasting process.
9.8 Notes and readings.
10. Classification.
10.1 Classification problems.
10.2 Evaluation of classification models.
10.3 Classification trees.
10.4 Bayesian methods.
10.5 Logistic regression.
10.6 Neural networks.
10.7 Support vector machines.
10.8 Notes and readings.
11. Association rules.
11.1 Motivation and structure of association rules.
11.2 Single-dimension association rules.
11.3 Apriori algorithm.
11.4 General association rules.
11.5 Notes and readings.
12. Clustering.
12.1 Clustering methods.
12.2 Partition methods.
12.3 Hierarchical methods.
12.4 Evaluation of clustering models.
12.5 Notes and readings.
III. BUSINESS INTELLIGENCE APPLICATIONS.
13. Marketing models.
13.1 Relational marketing.
13.2 Salesforce management.
13.3 Business cases.
13.4 Notes and readings.
14. Logistic and production models.
14.1 Supply chain optimization.
14.2 Optimization models for logistics planning.
14.3 Revenue management systems.
14.4 Business cases.
14.5 Notes and readings.
15. Data envelopment analysis.
15.1 Efficiency measures.
15.2 Efficient frontier.
15.3 The CCR model.
15.4 Identification of good operating practices.
15.5 Other models.
15.6 Notes and readings.
A Software tools.
B Dataset repositories.
References.
Index.
I. COMPONENTS OF THE DECISION MAKING PROCESS.
1. Business intelligence.
1.1 Effective and timely decisions.
1.2 Data, information and knowledge.
1.3 The role of mathematical models.
1.4 Business intelligence architectures.
1.5 Ethics and business intelligence.
1.6 Notes and readings.
2. Decision support systems.
2.1 Definition of system.
2.2 Representation of the decision making process.
2.3 Evolution of information.
2.4 Definition of decision support system.
2.5 Development of a decision support system.
2.6 Notes and readings.
3. Data warehousing.
3.1 Definition of data warehouse.
3.2 Data warehouse architecture.
3.3 Cubes and multidimensional analysis.
3.4 Notes and readings.
II. MATHEMATICAL MODELS AND METHODS.
4. Mathematical models for decision making.
4.1 Structure of mathematical models.
4.2 Development of a model.
4.3 Classes of models.
4.4 Notes and readings.
5. Data mining.
5.1 Definition of data mining.
5.2 Representation of input data.
5.3 Data mining process.
5.4 Analysis methodologies.
5.5 Notes and readings.
6. Data preparation.
6.1 Data validation.
6.2 Data transformation.
6.3 Data reduction.
7. Data exploration.
7.1 Univariate analysis.
7.2 Bivariate analysis.
7.3 Multivariate analysis.
7.4 Notes and readings.
8. Regression.
8.1 Structure of regression models.
8.2 Simple linear regression.
8.3 Multiple linear regression.
8.4 Validation of regression models.
8.5 Selection of predictive variables.
8.6 Notes and readings.
9. Time series.
9.1 Definition of time series.
9.2 Evaluating time series models.
9.3 Analysis of the components of time series.
9.4 Exponential smoothing models.
9.5 Autoregressive models.
9.6 Combination of predictive models.
9.7 The forecasting process.
9.8 Notes and readings.
10. Classification.
10.1 Classification problems.
10.2 Evaluation of classification models.
10.3 Classification trees.
10.4 Bayesian methods.
10.5 Logistic regression.
10.6 Neural networks.
10.7 Support vector machines.
10.8 Notes and readings.
11. Association rules.
11.1 Motivation and structure of association rules.
11.2 Single-dimension association rules.
11.3 Apriori algorithm.
11.4 General association rules.
11.5 Notes and readings.
12. Clustering.
12.1 Clustering methods.
12.2 Partition methods.
12.3 Hierarchical methods.
12.4 Evaluation of clustering models.
12.5 Notes and readings.
III. BUSINESS INTELLIGENCE APPLICATIONS.
13. Marketing models.
13.1 Relational marketing.
13.2 Salesforce management.
13.3 Business cases.
13.4 Notes and readings.
14. Logistic and production models.
14.1 Supply chain optimization.
14.2 Optimization models for logistics planning.
14.3 Revenue management systems.
14.4 Business cases.
14.5 Notes and readings.
15. Data envelopment analysis.
15.1 Efficiency measures.
15.2 Efficient frontier.
15.3 The CCR model.
15.4 Identification of good operating practices.
15.5 Other models.
15.6 Notes and readings.
A Software tools.
B Dataset repositories.
References.
Index.
