Book Image

The Unsupervised Learning Workshop

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

The Unsupervised Learning Workshop

By: Aaron Jones, Christopher Kruger, Benjamin Johnston

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.

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Table of Contents (11 chapters)
Preface
About the Book
1
1. Introduction to Clustering
1. Introduction to Clustering
Introduction
Unsupervised Learning versus Supervised Learning
Clustering
Introduction to k-means Clustering
Summary
Free Chapter
2
2. Hierarchical Clustering
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
3. Neighborhood Approaches and DBSCAN
3. Neighborhood Approaches and DBSCAN
Introduction
Clusters as Neighborhoods
Introduction to DBSCAN
DBSCAN versus k-means and Hierarchical Clustering
Summary
4
4. Dimensionality Reduction Techniques and PCA
4. Dimensionality Reduction Techniques and PCA
Introduction
What Is Dimensionality Reduction?
Overview of Dimensionality Reduction Techniques
Principal Component Analysis
Summary
5
5. Autoencoders
5. Autoencoders
Introduction
Fundamentals of Artificial Neural Networks
Autoencoders
Summary
6
6. t-Distributed Stochastic Neighbor Embedding
6. t-Distributed Stochastic Neighbor Embedding
Introduction
The MNIST Dataset
Stochastic Neighbor Embedding (SNE)
t-Distributed SNE
Interpreting t-SNE Plots
Summary
7
7. Topic Modeling
7. Topic Modeling
Introduction
Topic Models
Cleaning Text Data
Latent Dirichlet Allocation
Non-Negative Matrix Factorization
Summary
8
8. Market Basket Analysis
8. Market Basket Analysis
Introduction
Market Basket Analysis
Characteristics of Transaction Data
The Apriori Algorithm
Association Rules
Summary
9
9. Hotspot Analysis
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

Kernel density estimation is a classic statistical technique that is in the same family of techniques as the histogram. It allows the user to extrapolate out from sample data to make insights and predictions about the population of particular objects or events. This extrapolation comes in the form of a probability density function, which is nice because the results read as likelihoods or probabilities. The quality of this model is dependent on two parameters: the bandwidth value and the kernel function. As discussed, the most crucial component of leveraging kernel density estimation successfully is the setting of an optimal bandwidth. Optimal bandwidths are most frequently identified using grid search cross-validation with pseudo-log-likelihood as the scoring metric. What makes kernel density estimation great is both its simplicity and its applicability to so many fields.

It is routine to find kernel density estimation models in criminology, epidemiology, meteorology, and...

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