Applied Unsupervised Learning with R

封面

版权页

Preface

About the Book

Chapter 1 Introduction to Clustering Methods

Introduction

Introduction to Clustering

Introduction to the Iris Dataset

Introduction to k-means Clustering

Introduction to k-means Clustering with Built-In Functions

Introduction to Market Segmentation

Introduction to k-medoids Clustering

Summary

Chapter 2 Advanced Clustering Methods

Introduction

Introduction to k-modes Clustering

Introduction to Density-Based Clustering (DBSCAN)

Summary

Chapter 3 Probability Distributions

Introduction

Basic Terminology of Probability Distributions

Introduction to Kernel Density Estimation

Introduction to the Kolmogorov-Smirnov Test

Summary

Chapter 4 Dimension Reduction

Introduction

Market Basket Analysis

Summary

Chapter 5 Data Comparison Methods

Introduction

Analytic Signatures

Comparison of Signatures

Latent Variable Models – Factor Analysis

Summary

Chapter 6 Anomaly Detection

Introduction

Univariate Outlier Detection

Kernel Density

Summary

Appendix

Chapter 1: Introduction to Clustering Methods

Chapter 2: Advanced Clustering Methods

Chapter 3: Probability Distributions

Chapter 4: Dimension Reduction

Chapter 5: Data Comparison Methods