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

Introduction

In the last chapter, the discussion focused on preparing data for modeling using dimensionality reduction and autoencoding. Large feature sets can be problematic when it comes to modeling because of multicollinearity and extensive computation and can thereby hinder real-time prediction. Dimensionality reduction using principal component analysis is one antidote to that problem. Similarly, autoencoders seek to find optimal feature encodings. You can think of autoencoders as a means of identifying quality interaction terms for the dataset. Let's now move past dimensionality reduction and look at some real-world modeling techniques.

Topic modeling is one facet of Natural Language Processing (NLP), the field of computer science exploring the syntactic and semantic analysis of natural language, which has been increasing in popularity with the increased availability of textual datasets. NLP can deal with language in almost any form, including text, speech, and images...

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