Data Mining and Statistics

networks.8.1 General information on neural networks.8.2 Structure of a neural network.8.3 Choosing the learning sample.8.4 Some empirical rules for network design.8.5 Data normalization.8.6 Learning algorithms.8.7 The main neural networks.9. Cluster analysis.9.1 Definition of clustering.9.2 Applications of clustering.9.3 Complexity of clustering.9.4 Clustering structures.9.5 Some methodological considerations.9.6 Comparison of factor analysis and clustering.9.7 Within-cluster and between-cluster sum of squares.9.8 Measurements of clustering quality.9.9 Partitioning methods.9.10 Agglomerative hierarchical clustering.9.11 Hybrid clustering methods.9.12 Neural clustering.9.13 Clustering by similarity aggregation.9.14 Clustering of numeric variables.9.15 Overview of clustering methods.10. Association analysis.10.1 Principles.10.2 Using taxonomy.10.3 Using supplementary variables.10.4 Applications.10.5 Example of use.11. Classification and prediction methods.11.1 Introduction.11.2 Inductive and transductive methods.11.3 Overview of classification and prediction methods.11.4 Classification by decision tree.11.5 Prediction by decision tree.11.6 Classification by discriminant analysis.11.7 Prediction by linear regression.11.8 Classification by logistic regression.11.9 Developments in logistic regression.11.10 Bayesian methods.11.11 Classification and prediction by neural networks.11.12 Classification by support vector machines.11.13 Prediction by genetic algorithms.11.14 Improving the performance of a predictive model.11.15 Bootstrapping and ensemble methods.11.16 Using classification and prediction methods.12. An application of data mining: scoring.12.1 The different types of score.12.2 Using propensity scores and risk scores.12.3 Methodology.12.4 Implementing a strategic score.12.5 Implementing an operational score.12.6 Scoring solutions used in a business.12.7 An example of credit scoring (data preparation).12.8 An example of credit scoring (modeling by logistic regression).12.9 An example of credit scoring (modeling by DISQUAL discriminant analysis).12.10 A brief history of credit scoring.13. Factors for success in a data mining project.13.1 The subject.13.2 The people.13.3 The data.13.4 The IT systems.13.5 The business culture.13.6 Data mining: eight common misconceptions.13.7 Return on investment.14. Text mining.14.1 Definition of text mining.14.2 Text sources used.14.3 Using text mining.14.4 Information retrieval.14.5 Information extraction.14.6 Multi-type data mining.15. Web mining.15.1 The aims of web mining.15.2 Global analyses.15.3 Individual analyses.15.4 Personal analysis.Appendix A. Elements of statistics.Appendix B. Further reading.Index.