Business Intelligence: Data Mining and Optimization for Decision Making explores a broad spectrum of topics currently dispersed throughout data mining and business books. In bringing these topics together for the first time, the book provides readers with an introduction and practical guide to the mathematical models and analysis methodologies vital to business intelligence. Starting from a thorough description of decision support systems and data warehousing, the book then moves to a detailed presentation of methods for data mining and inductive learning data. Finally, the book considers…mehr
Business Intelligence: Data Mining and Optimization for Decision Making explores a broad spectrum of topics currently dispersed throughout data mining and business books. In bringing these topics together for the first time, the book provides readers with an introduction and practical guide to the mathematical models and analysis methodologies vital to business intelligence. Starting from a thorough description of decision support systems and data warehousing, the book then moves to a detailed presentation of methods for data mining and inductive learning data. Finally, the book considers applications of data mining to relational marketing, models for optimizing the supply chain, and analytical methods for performance evaluation.Data Mining und Optimierung zur Erleichterung von Entscheidungen: Der Autor dieses Bandes hat Informationen zu diesem Thema zusammengefasst und aufbereitet, die Sie sonst mühsam in der weit verstreuten Fachliteratur suchen müssten. Mathematische Modelle und Analysenverfahren werden gut verständlich eingeführt und anhand von Beispielen und Fallstudien aus der Praxis erläutert.
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.
Inhaltsangabe
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.
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.
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