Data Mining Algorithms Explained Using R [Hardcover]

Data Mining Algorithms is a practical, technically-oriented guide to data mining algorithms that covers the most important algorithms for building classification, regression, and clustering models, as well as techniques used for attribute selection and transformation, model quality evaluation, and creating model ensembles. The author presents many of the important topics and methodologies widely used in data mining, whilst demonstrating the internal operation and usage of data mining algorithms using examples in R.

Acknowledgements xix

Preface xxi

References xxxi

Part I Preliminaries 1

1 Tasks 3

1.1 Introduction 3

1.2 Inductive learning tasks 5

1.3 Classification 9

1.4 Regression 14

1.5 Clustering 16

1.6 Practical issues 19

1.7 Conclusion 20

1.8 Further readings 21

References 22

2 Basic statistics 23

2.1 Introduction 23

2.2 Notational conventions 24

2.3 Basic statistics as modeling 24

2.4 Distribution description 25

2.5 Relationship detection 47

2.6 Visualization 62

2.7 Conclusion 65

2.8 Further readings 66

References 67

Part II Classification 69

3 Decision trees 71

3.1 Introduction 71

3.2 Decision tree model 72

3.3 Growing 76

3.4 Pruning 90

3.5 Prediction 103

3.6 Weighted instances 105

3.7 Missing value handlinglSÃ