Business Intelligence
Data Mining and Optimization for Decision Making
(Sprache: Englisch)
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...
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Produktinformationen zu „Business Intelligence “
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.
Klappentext zu „Business Intelligence “
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. The term implies having a comprehensive knowledge of all factors 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 an introduction and practical guide to the mathematical models and analysis methodologies vital to business intelligence.
This book:
* Combines detailed coverage with a practical guide to the mathematical models and analysis methodologies of business intelligence.
* Covers all the hot topics such as data warehousing, data mining and its applications, machine learning, classification, supply optimization models, decision support systems, and analytical methods for performance evaluation.
* Is made accessible to readers through the careful definition and introduction of each concept, followed by the extensive use of examples and numerous real-life case studies.
* Explains how to utilise mathematical models and analysis models to make effective and good quality business decisions.
This book is aimed at postgraduate students following data analysis and data mining courses.
Researchers looking for a systematic and broad coverage of topics in operations research and mathematical models for decision-making will find this an invaluable guide.
Inhaltsverzeichnis zu „Business Intelligence “
PrefaceI. 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
... mehr
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
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
... weniger
Autoren-Porträt von Carlo Vercellis
Carlo Vercellis - School of Management, Politecnico di Milano, ItalyAs 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.
Bibliographische Angaben
- Autor: Carlo Vercellis
- 2009, 1. Auflage, 448 Seiten, Maße: 15 x 22,6 cm, Kartoniert (TB), Englisch
- Verlag: Wiley & Sons
- ISBN-10: 0470511397
- ISBN-13: 9780470511398
- Erscheinungsdatum: 27.03.2009
Sprache:
Englisch
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