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Home > Data > Machine Learning > Applied Unsupervised Learning with Python
Applied Unsupervised Learning with Python
Applied Unsupervised Learning with Python

Applied Unsupervised Learning with Python: Discover hidden patterns and relationships in unstructured data with Python

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Applied Unsupervised Learning with Python

Chapter 2. Hierarchical Clustering

Note

Learning Objectives

By the end of this chapter, you will be able to:

  • Implement the hierarchical clustering algorithm from scratch by using packages

  • Perform agglomerative clustering

  • Compare k-means with hierarchical clustering

Note

In this chapter, we will use hierarchical clustering to build stronger groupings which make more logical sense.

Introduction

In this chapter, we will expand on the basic ideas that we built in Chapter 1, Introduction to Clustering, by surrounding clustering with the concept of similarity. Once again, we will be implementing forms of the Euclidean distance to capture the notion of similarity. It is important to bear in mind that the Euclidean distance just happens to be one of the most popular distance metrics and not the only one! Through these distance metrics, we will expand on the simple neighbor calculations that we explored in the previous chapter by introducing the concept of hierarchy. By using hierarchy to convey clustering information, we can build stronger groupings that make more logical sense.

Clustering Refresher

Chapter 1, Introduction to Clustering, covered both the high-level intuition and in-depth details of one of the most basic clustering algorithms: k-means. While it is indeed a simple approach, do not discredit it; it will be a valuable addition to your toolkit as you continue your exploration of the unsupervised learning world. In many real-world use cases, companies experience groundbreaking discoveries through the simplest methods, such as k-means or linear regression (for supervised learning). As a refresher, let's quickly walk through what clusters are and how k-means works to find them:

Figure 2.1: The attributes that separate supervised and unsupervised problems

If you were given a random collection of data without any guidance, you would likely start your exploration using basic statistics – for example, what the mean, median, and mode values are of each of the features. Remember that, from a high-level data model that simply exists, knowing whether it is supervised...

The Organization of Hierarchy

Both the natural and human-made world contain many examples of organizing systems into hierarchies and why, for the most part, it makes a lot of sense. A common representation that is developed from these hierarchies can be seen in tree-based data structures. Imagine that you had a parent node with any number of child nodes that could subsequently be parent nodes themselves. By organizing concepts into a tree structure, you can build an information-dense diagram that clearly shows how things are related to their peers and their larger abstract concepts.

An example from the natural world to help illustrate this concept can be seen in how we view the hierarchy of animals, which goes from parent classes to individual species:

Figure 2.2: Navigating the relationships of animal species in a hierarchical tree structure

In Figure 2.2, you can see an example of how relational information between varieties of animals can be easily mapped out in a way that both saves space...

Introduction to Hierarchical Clustering

Until this point, we have shown that hierarchies can be excellent structures in which to organize information that clearly show nested relationships among data points. While this is helpful in gaining an understanding of the parent/child relationships between items, it can also be very handy when forming clusters. Expanding on the animal example of the prior section, imagine that you were simply presented with two features of animals: their height (measured from the tip of the nose to the end of the tail) and their weight. Using this information, you then have to recreate the same structure in order to identify which records in your dataset correspond to dogs or cats, as well as their relative subspecies.

Since you are only given animal heights and weights, you won't be able to extrapolate the specific names of each species. However, by analyzing the features that you have been provided, you can develop a structure within the data that serves as an...

Linkage

In Exercise 7, Building a Hierarchy, you implemented hierarchical clustering using what is known as Centroid Linkage. Linkage is the concept of determining how you can calculate the distances between clusters and is dependent on the type of problem you are facing. Centroid linkage was chosen for the first activity as it essentially mirrors the new centroid search that we used in k-means. However, this is not the only option when it comes to clustering data points together. Two other popular choices for determining distances between clusters are single linkage and complete linkage.

Single Linkage works by finding the minimum distance between a pair of points between two clusters as its criteria for linkage. Put simply, it essentially works by combining clusters based on the closest points between the two clusters. This is expressed mathematically as follows:

dist(a,b) = min( dist( a[i]), b[j] ) )

Complete Linkage is the opposite of single linkage and it works by finding the maximum distance...

Agglomerative versus Divisive Clustering

Our instances of hierarchical clustering so far have all been agglomerative – that is, they have been built from the bottom up. While this is typically the most common approach for this type of clustering, it is important to know that it is not the only way a hierarchy can be created. The opposite hierarchical approach, that is, built from the top up, can also be used to create your taxonomy. This approach is called Divisive Hierarchical Clustering and works by having all the data points in your dataset in one massive cluster. Many of the internal mechanics of the divisive approach will prove to be quite similar to the agglomerative approach:

Figure 2.15: Agglomerative versus divisive hierarchical clustering

As with most problems in unsupervised learning, deciding the best approach is often highly dependent on the problem you are faced with solving.

Imagine that you are an entrepreneur who has just bought a new grocery store and needs to stock it with...

k-means versus Hierarchical Clustering

Now that we have expanded our understanding of how k-means clustering works, it is important to explore where hierarchical clustering fits into the picture. As mentioned in the linkage criteria section, there is some potential direct overlap when it comes to grouping data points together using centroids. Universal to all of the approaches mentioned so far, is also the use of a distance function to determine similarity. Due to our in-depth exploration in the previous chapter, we have kept using the Euclidean distance, but we understand that any distance function can be used to determine similarity.

In practice, here are some quick highlights for choosing one clustering method over another:

  • Hierarchical clustering benefits from not needing to pass in an explicit "k" number of clusters apriori. This means that you can find all the potential clusters and decide which clusters make the most sense after the algorithm has completed.

  • k-means clustering benefits...

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 compared to 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...

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Page 1 of 9

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Key benefits

  • Learn how to select the most suitable Python library to solve your problem
  • Compare k-Nearest Neighbor (k-NN) and non-parametric methods and decide when to use them
  • Explore the applications of neural networks using real-world datasets

Description

Unsupervised learning is a useful and practical solution in situations where labeled data is not available. Applied Unsupervised Learning with Python guides you in learning the best practices for using unsupervised learning techniques in tandem with Python libraries and extracting meaningful information from unstructured data. The book begins by explaining how basic clustering works to find similar data points in a set. Once you are well-versed with the k-means algorithm and how it operates, you’ll learn what dimensionality reduction is and where to apply it. As you progress, you’ll learn various neural network techniques and how they can improve your model. While studying the applications of unsupervised learning, you will also understand how to mine topics that are trending on Twitter and Facebook and build a news recommendation engine for users. Finally, you will be able to put your knowledge to work through interesting activities such as performing a Market Basket Analysis and identifying relationships between different products. By the end of this book, you will have the skills you need to confidently build your own models using Python.

Who is this book for?

This course is designed for developers, data scientists, and machine learning enthusiasts who are interested in unsupervised learning. Some familiarity with Python programming along with basic knowledge of mathematical concepts including exponents, square roots, means, and medians will be beneficial.

What you will learn

  • Understand the basics and importance of clustering
  • Build k-means, hierarchical, and DBSCAN clustering algorithms from scratch with built-in packages
  • Explore dimensionality reduction and its applications
  • Use scikit-learn (sklearn) to implement and analyze principal component analysis (PCA) on the Iris dataset
  • Employ Keras to build autoencoder models for the CIFAR-10 dataset
  • Apply the Apriori algorithm with machine learning extensions (Mlxtend) to study transaction data
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Concepts :
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Applied Supervised Learning with Python
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Table of Contents

9 Chapters
Introduction to Clustering Chevron down icon Chevron up icon
Introduction to Clustering
Introduction
Unsupervised Learning versus Supervised Learning
Clustering
Introduction to k-means Clustering
Activity 1: Implementing k-means Clustering
Summary
Hierarchical Clustering Chevron down icon Chevron up icon
Hierarchical Clustering
Introduction
Clustering Refresher
The Organization of Hierarchy
Introduction to Hierarchical Clustering
Linkage
Agglomerative versus Divisive Clustering
k-means versus Hierarchical Clustering
Summary
Neighborhood Approaches and DBSCAN Chevron down icon Chevron up icon
Neighborhood Approaches and DBSCAN
Introduction
Introduction to DBSCAN
DBSCAN Versus k-means and Hierarchical Clustering
Summary
Dimension Reduction and PCA Chevron down icon Chevron up icon
Dimension Reduction and PCA
Introduction
Overview of Dimensionality Reduction Techniques
PCA
Summary
Autoencoders Chevron down icon Chevron up icon
Autoencoders
Introduction
Fundamentals of Artificial Neural Networks
Autoencoders
Summary
t-Distributed Stochastic Neighbor Embedding (t-SNE) Chevron down icon Chevron up icon
t-Distributed Stochastic Neighbor Embedding (t-SNE)
Introduction
Stochastic Neighbor Embedding (SNE)
t-Distributed SNE
Interpreting t-SNE Plots
Summary
Topic Modeling Chevron down icon Chevron up icon
Topic Modeling
Introduction
Cleaning Text Data
Latent Dirichlet Allocation
Non-Negative Matrix Factorization
Summary
Market Basket Analysis Chevron down icon Chevron up icon
Market Basket Analysis
Introduction
Market Basket Analysis
Characteristics of Transaction Data
Apriori Algorithm
Association Rules
Summary
Hotspot Analysis Chevron down icon Chevron up icon
Hotspot Analysis
Introduction
Kernel Density Estimation
Hotspot Analysis
Summary

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About the 3 authors

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Benjamin Johnston
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Benjamin Johnston is a senior data scientist for one of the world's leading data-driven MedTech companies and is involved in the development of innovative digital solutions throughout the entire product development pathway, from problem definition to solution research and development, through to final deployment. He is currently completing his Ph.D. in machine learning, specializing in image processing and deep convolutional neural networks. He has more than 10 years of experience in medical device design and development, working in a variety of technical roles, and holds first-class honors bachelor's degrees in both engineering and medical science from the University of Sydney, Australia.
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Profile icon Christopher Kruger
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Christopher Kruger is a practicing data scientist and AI researcher. He has managed applied machine learning projects across multiple industries while mentoring junior team members on best practices. His primary focus is on pushing both business practicality as well as academic rigor in every project. Chris is currently developing research in the computer vision space.
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Aaron Jones
Aaron Jones is a full-time senior data scientist and consultant. He has built models and data products while working in retail, media, and environmental science. Aaron is based in Seattle, Washington and has a particular interest in clustering algorithms, natural language processing, and Bayesian statistics.
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We want you to be happy with your purchase from Packtpub.com. We will not hassle you with returning print books to us. If the print book you receive from us is incorrect, damaged, doesn't work or is unacceptably late, please contact Customer Relations Team on customercare@packt.com with the order number and issue details as explained below:

  1. If you ordered (eBook, Video or Print Book) incorrectly or accidentally, please contact Customer Relations Team on customercare@packt.com within one hour of placing the order and we will replace/refund you the item cost.
  2. Sadly, if your eBook or Video file is faulty or a fault occurs during the eBook or Video being made available to you, i.e. during download then you should contact Customer Relations Team within 14 days of purchase on customercare@packt.com who will be able to resolve this issue for you.
  3. You will have a choice of replacement or refund of the problem items.(damaged, defective or incorrect)
  4. Once Customer Care Team confirms that you will be refunded, you should receive the refund within 10 to 12 working days.
  5. If you are only requesting a refund of one book from a multiple order, then we will refund you the appropriate single item.
  6. Where the items were shipped under a free shipping offer, there will be no shipping costs to refund.

On the off chance your printed book arrives damaged, with book material defect, contact our Customer Relation Team on customercare@packt.com within 14 days of receipt of the book with appropriate evidence of damage and we will work with you to secure a replacement copy, if necessary. Please note that each printed book you order from us is individually made by Packt's professional book-printing partner which is on a print-on-demand basis.

What tax is charged? Chevron down icon Chevron up icon

Currently, no tax is charged on the purchase of any print book (subject to change based on the laws and regulations). A localized VAT fee is charged only to our European and UK customers on eBooks, Video and subscriptions that they buy. GST is charged to Indian customers for eBooks and video purchases.

What payment methods can I use? Chevron down icon Chevron up icon

You can pay with the following card types:

  1. Visa Debit
  2. Visa Credit
  3. MasterCard
  4. PayPal
What is the delivery time and cost of print books? Chevron down icon Chevron up icon

Shipping Details

USA:

'

Economy: Delivery to most addresses in the US within 10-15 business days

Premium: Trackable Delivery to most addresses in the US within 3-8 business days

UK:

Economy: Delivery to most addresses in the U.K. within 7-9 business days.
Shipments are not trackable

Premium: Trackable delivery to most addresses in the U.K. within 3-4 business days!
Add one extra business day for deliveries to Northern Ireland and Scottish Highlands and islands

EU:

Premium: Trackable delivery to most EU destinations within 4-9 business days.

Australia:

Economy: Can deliver to P. O. Boxes and private residences.
Trackable service with delivery to addresses in Australia only.
Delivery time ranges from 7-9 business days for VIC and 8-10 business days for Interstate metro
Delivery time is up to 15 business days for remote areas of WA, NT & QLD.

Premium: Delivery to addresses in Australia only
Trackable delivery to most P. O. Boxes and private residences in Australia within 4-5 days based on the distance to a destination following dispatch.

India:

Premium: Delivery to most Indian addresses within 5-6 business days

Rest of the World:

Premium: Countries in the American continent: Trackable delivery to most countries within 4-7 business days

Asia:

Premium: Delivery to most Asian addresses within 5-9 business days

Disclaimer:
All orders received before 5 PM U.K time would start printing from the next business day. So the estimated delivery times start from the next day as well. Orders received after 5 PM U.K time (in our internal systems) on a business day or anytime on the weekend will begin printing the second to next business day. For example, an order placed at 11 AM today will begin printing tomorrow, whereas an order placed at 9 PM tonight will begin printing the day after tomorrow.


Unfortunately, due to several restrictions, we are unable to ship to the following countries:

  1. Afghanistan
  2. American Samoa
  3. Belarus
  4. Brunei Darussalam
  5. Central African Republic
  6. The Democratic Republic of Congo
  7. Eritrea
  8. Guinea-bissau
  9. Iran
  10. Lebanon
  11. Libiya Arab Jamahriya
  12. Somalia
  13. Sudan
  14. Russian Federation
  15. Syrian Arab Republic
  16. Ukraine
  17. Venezuela