2. Hierarchical Clustering | The Unsupervised Learning Workshop

Book Overview & Buying
Table Of Contents

The Unsupervised Learning Workshop

By : Aaron Jones , Richard Brooker, John Wesley Doyle , Priyanjit Ghosh, Sani Kamal, Ashish Pratik Patil , Philip Solomon, Geetank Raipuria, Christopher Kruger , Benjamin Johnston

4.3 (6)

Buy this Book

The Unsupervised Learning Workshop

4.3 (6)

By: Aaron Jones , Richard Brooker, John Wesley Doyle , Priyanjit Ghosh, Sani Kamal, Ashish Pratik Patil , Philip Solomon, Geetank Raipuria, Christopher Kruger , Benjamin Johnston

Buy this Book

Overview of this book

Do you find it difficult to understand how popular companies like WhatsApp and Amazon find valuable insights from large amounts of unorganized data? The Unsupervised Learning Workshop will give you the confidence to deal with cluttered and unlabeled datasets, using unsupervised algorithms in an easy and interactive manner. The book starts by introducing the most popular clustering algorithms of unsupervised learning. You'll find out how hierarchical clustering differs from k-means, along with understanding how to apply DBSCAN to highly complex and noisy data. Moving ahead, you'll use autoencoders for efficient data encoding. As you progress, you’ll use t-SNE models to extract high-dimensional information into a lower dimension for better visualization, in addition to working with topic modeling for implementing natural language processing (NLP). In later chapters, you’ll find key relationships between customers and businesses using Market Basket Analysis, before going on to use Hotspot Analysis for estimating the population density of an area. By the end of this book, you’ll be equipped with the skills you need to apply unsupervised algorithms on cluttered datasets to find useful patterns and insights.

Preface

About the Book

1. Introduction to Clustering

Introduction

Unsupervised Learning versus Supervised Learning

Clustering

Introduction to k-means Clustering

Summary

Free Chapter

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

9. Hotspot Analysis

Summary

In this chapter, we discussed how hierarchical clustering works and where it may be best employed. In particular, we discussed various aspects of how clusters can be subjectively chosen through the evaluation of a dendrogram plot. This is a huge advantage over k-means clustering if you have absolutely no idea of what you're looking for in the data. Two key parameters that drive the success of hierarchical clustering were also discussed: the agglomerative versus divisive approach and linkage criteria. Agglomerative clustering takes a bottom-up approach by recursively grouping nearby data together until it results in one large cluster. Divisive clustering takes a top-down approach by starting with the one large cluster and recursively breaking it down until each data point falls into its own cluster. Divisive clustering has the potential to be more accurate since it has a complete view of the data from the start; however, it adds a layer of complexity that can decrease the stability and increase the runtime.

Linkage criteria grapples with the concept of how distance is calculated between candidate clusters. We have explored how centroids can make an appearance again beyond k-means clustering, as well as single and complete linkage criteria. Single linkage finds cluster distances by comparing the closest points in each cluster, while complete linkage finds cluster distances by comparing more distant points in each cluster. With the knowledge that you have gained in this chapter, you are now able to evaluate how both k-means and hierarchical clustering can best fit the challenge that you are working on.

While hierarchical clustering can result in better performance than k-means due to its increased complexity, please remember that more complexity is not always good. Your duty as a practitioner of unsupervised learning is to explore all the options and identify the solution that is both resource-efficient and performant. In the next chapter, we will cover a clustering approach that will serve us best when it comes to highly complex and noisy data: Density-Based Spatial Clustering of Applications with Noise.

The Unsupervised Learning Workshop

By : Aaron Jones , Richard Brooker, John Wesley Doyle , Priyanjit Ghosh, Sani Kamal, Ashish Pratik Patil , Philip Solomon, Geetank Raipuria, Christopher Kruger , Benjamin Johnston

The Unsupervised Learning Workshop

By: Aaron Jones , Richard Brooker, John Wesley Doyle , Priyanjit Ghosh, Sani Kamal, Ashish Pratik Patil , Philip Solomon, Geetank Raipuria, Christopher Kruger , Benjamin Johnston

Overview of this book

Summary

Confirmation

Buy this book with your credits?

Submit Your Feedback

Create a Free Account To Continue Reading

Sign in to activate your 7-day free access