Data Mining Algorithms: Explained Using R

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.

Table of Contents

Part I Preliminaries
Chapter 1 Tasks
Chapter 2 Basic statistics

Part II Classification
Chapter 3 Decision trees
Chapter 4 Naïve Bayes classifier
Chapter 5 Linear classification
Chapter 6 Misclassification costs
Chapter 7 Classification model evaluation

Part III Regression
Chapter 8 Linear regression
Chapter 9 Regression trees
Chapter 10 Regression model evaluation

Part IV Clustering
Chapter 11 (Dis)similarity measures
Chapter 12 k-Centers clustering
Chapter 13 Hierarchical clustering
Chapter 14 Clustering model evaluation

Part V Getting Better Models
Chapter 15 Model ensembles
Chapter 16 Kernel methods
Chapter 17 Attribute transformation
Chapter 18 Discretization
Chapter 19 Attribute selection
Chapter 20 Case studies

Appendix A Notation
Appendix B R packages
Appendix C Datasets