The Unsupervised Learning Workshop

封面

版权信息

Preface

1. Introduction to Clustering

Introduction

Unsupervised Learning versus Supervised Learning

Clustering

Introduction to k-means Clustering

Summary

2. Hierarchical Clustering

Introduction

Clustering Refresher

The Organization of the Hierarchy

Introduction to Hierarchical Clustering

Linkage

Agglomerative versus Divisive Clustering

k-means versus Hierarchical Clustering

Summary

3. Neighborhood Approaches and DBSCAN

Introduction

Clusters as Neighborhoods

Introduction to DBSCAN

DBSCAN versus k-means and Hierarchical Clustering

Summary

4. Dimensionality Reduction Techniques and PCA

Introduction

What Is Dimensionality Reduction?

Overview of Dimensionality Reduction Techniques

Principal Component Analysis

Summary

5. Autoencoders

Introduction

Fundamentals of Artificial Neural Networks

Autoencoders

Summary

6. t-Distributed Stochastic Neighbor Embedding

Introduction

The MNIST Dataset

Stochastic Neighbor Embedding (SNE)

t-Distributed SNE

Interpreting t-SNE Plots

Summary

7. Topic Modeling

Introduction

Topic Models

Cleaning Text Data

Latent Dirichlet Allocation

Non-Negative Matrix Factorization

Summary

8. Market Basket Analysis

Introduction

Market Basket Analysis

Characteristics of Transaction Data

The Apriori Algorithm

Association Rules

Summary

9. Hotspot Analysis

Introduction

Spatial Statistics

Kernel Density Estimation

Hotspot Analysis

Summary

Appendix

1. Introduction to Clustering

2. Hierarchical Clustering

3. Neighborhood Approaches and DBSCAN

4. Dimensionality Reduction Techniques and PCA

5. Autoencoders

6. t-Distributed Stochastic Neighbor Embedding

7. Topic Modeling

8. Market Basket Analysis