Book Image

Applied Unsupervised Learning with Python

By : Benjamin Johnston, Aaron Jones, Christopher Kruger
Book Image

Applied Unsupervised Learning with Python

By: Benjamin Johnston, Aaron Jones, Christopher Kruger

Overview of this book

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.

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Table of Contents (12 chapters)
Applied Unsupervised Learning with Python
Applied Unsupervised Learning with Python
Preface
Preface
Free Chapter
1
Introduction to Clustering
Introduction to Clustering
Introduction
Unsupervised Learning versus Supervised Learning
Clustering
Introduction to k-means Clustering
Activity 1: Implementing k-means Clustering
Summary
2
Hierarchical Clustering
Hierarchical Clustering
Introduction
Clustering Refresher
The Organization of Hierarchy
Introduction to Hierarchical Clustering
Linkage
Agglomerative versus Divisive Clustering
k-means versus Hierarchical Clustering
Summary
3
Neighborhood Approaches and DBSCAN
Neighborhood Approaches and DBSCAN
Introduction
Introduction to DBSCAN
DBSCAN Versus k-means and Hierarchical Clustering
Summary
4
Dimension Reduction and PCA
Dimension Reduction and PCA
Introduction
Overview of Dimensionality Reduction Techniques
PCA
Summary
5
Autoencoders
Autoencoders
Introduction
Fundamentals of Artificial Neural Networks
Autoencoders
Summary
6
t-Distributed Stochastic Neighbor Embedding (t-SNE)
t-Distributed Stochastic Neighbor Embedding (t-SNE)
Introduction
Stochastic Neighbor Embedding (SNE)
t-Distributed SNE
Interpreting t-SNE Plots
Summary
7
Topic Modeling
Topic Modeling
Introduction
Cleaning Text Data
Latent Dirichlet Allocation
Non-Negative Matrix Factorization
Summary
8
Market Basket Analysis
Market Basket Analysis
Introduction
Market Basket Analysis
Characteristics of Transaction Data
Apriori Algorithm
Association Rules
Summary
9
Hotspot Analysis
Hotspot Analysis
Introduction
Kernel Density Estimation
Hotspot Analysis
Summary
Appendix
Appendix
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
Chapter 9: Hotspot Analysis

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...

Previous Section
End of Section 8
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