| Alan H. Fielding is Senior Lecturer in the Division of Biology at Manchester Metropolitan University. |
| Preface 1 Introduction 1.1 Background 1.2 Book structure 1.3 Classification 1.4 Clustering 1.5 Structures in data 1.6 Glossary 1.7 Recommended reading and other resources 2 Exploratory data analysis 2.1 Background 2.2 Dimensionality 2.3 Goodness of fit testing 2.4 Graphical methods 2.5 Variance-based data projections 2.6 Distance-based data projections 2.7 Other projection methods 2.8 Other methods 2.9 Data dredging 2.10 Example EDA analysis 3 Cluster analysis 3.1 Background 3.2 Distance and similarity measures 3.3 Partitioning methods 3.4 Agglomerative hierarchical methods 3.5 How many groups are there? 3.6 Divisive hierarchical methods 3.7 Two-way clustering and gene shaving 3.8 Recommended reading 3.9 Example analyses 4 Introduction to classification 4.1 Background 4.2 Black-box classifiers 4.3 Nature of a classifier 4.4 No-free-lunch 4.5 Bias and variance 4.6 Variable (feature) selection 4.7 Multiple classifiers 4.8 Why do classifiers fail? 4.9 Generalisation 4.1o Types of classifier 5 Classification algorithms 1 5.1 Background 5.2 Naive Bayes 5.3 Discriminant analysis 5.4 Logistic regression 5.5 Discriminant analysis or logistic regression? 5.6 Generalised additive models 5.7 Summary 6 Other classification methods 6.1 Background 6.2 Decision trees 6.3 Support vector machines 6.4 Artificial neural networks 6.5 Genetic algorithms 6.6 Others 6.7 Where next? 7 Classification accuracy 7.1 Background 7.2 Appropriate metrics 7.3 Binary accuracy measures 7.4 Appropriate testing data 7.5 Decision thresholds 7.6 Example 7.7 ROC plots 7.8 Incorporating costs 7.9 Comparing classifiers 7.10 Recommended reading Appendix References Index |
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