Applied Unsupervised Learning with Python

封面

版权页

Preface

About

About the Book

Chapter 1 Introduction to Clustering

Introduction

Unsupervised Learning versus Supervised Learning

Clustering

Introduction to k-means Clustering

Summary

Chapter 2 Hierarchical Clustering

Introduction

Clustering Refresher

The Organization of Hierarchy

Introduction to Hierarchical Clustering

Linkage

Agglomerative versus Divisive Clustering

k-means versus Hierarchical Clustering

Summary

Chapter 3 Neighborhood Approaches and DBSCAN

Introduction

Introduction to DBSCAN

DBSCAN Versus k-means and Hierarchical Clustering

Summary

Chapter 4 Dimension Reduction and PCA

Introduction

Overview of Dimensionality Reduction Techniques

PCA

Summary

Chapter 5 Autoencoders

Introduction

Fundamentals of Artificial Neural Networks

Autoencoders

Summary

Chapter 6 t-Distributed Stochastic Neighbor Embedding (t-SNE)

Introduction

Stochastic Neighbor Embedding (SNE)

t-Distributed SNE

Interpreting t-SNE Plots

Summary

Chapter 7 Topic Modeling

Introduction

Cleaning Text Data

Latent Dirichlet Allocation

Non-Negative Matrix Factorization

Summary

Chapter 8 Market Basket Analysis

Introduction

Market Basket Analysis

Characteristics of Transaction Data

Apriori Algorithm

Association Rules

Summary

Chapter 9 Hotspot Analysis

Introduction

Kernel Density Estimation

Hotspot Analysis

Summary

Appendix

About

Chapter 1: Introduction to Clustering

Chapter 2: Hierarchical Clustering

Chapter 3: Neighborhood Approaches and DBSCAN

Chapter 4: Dimension Reduction and PCA

Chapter 5: Autoencoders

Chapter 6: t-Distributed Stochastic Neighbor Embedding (t-SNE)

Chapter 7: Topic Modeling

Chapter 8: Market Basket Analysis