Book Image

Hands-On Unsupervised Learning with Python

By : Giuseppe Bonaccorso
Book Image

Hands-On Unsupervised Learning with Python

By: Giuseppe Bonaccorso

Overview of this book

Unsupervised learning is about making use of raw, untagged data and applying learning algorithms to it to help a machine predict its outcome. With this book, you will explore the concept of unsupervised learning to cluster large sets of data and analyze them repeatedly until the desired outcome is found using Python. This book starts with the key differences between supervised, unsupervised, and semi-supervised learning. You will be introduced to the best-used libraries and frameworks from the Python ecosystem and address unsupervised learning in both the machine learning and deep learning domains. You will explore various algorithms, techniques that are used to implement unsupervised learning in real-world use cases. You will learn a variety of unsupervised learning approaches, including randomized optimization, clustering, feature selection and transformation, and information theory. You will get hands-on experience with how neural networks can be employed in unsupervised scenarios. You will also explore the steps involved in building and training a GAN in order to process images. By the end of this book, you will have learned the art of unsupervised learning for different real-world challenges.

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Table of Contents (12 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Getting Started with Unsupervised Learning
Getting Started with Unsupervised Learning
Technical requirements
Why do we need machine learning?
Types of machine learning algorithm
Why Python for data science and machine learning?
Summary
Questions
Further reading
2
Clustering Fundamentals
Clustering Fundamentals
Technical requirements
Introduction to clustering
K-means
Analysis of the Breast Cancer Wisconsin dataset
Evaluation metrics
K-Nearest Neighbors
Vector Quantization
Summary
Questions
Further reading
3
Advanced Clustering
Advanced Clustering
Technical requirements
Spectral clustering
Mean shift
DBSCAN
K-medoids
Online clustering
Summary
Questions
Further reading
4
Hierarchical Clustering in Action
Hierarchical Clustering in Action
Technical requirements
Cluster hierarchies
Agglomerative clustering
Analyzing a dendrogram
Cophenetic correlation as a performance metric
Agglomerative clustering on the Water Treatment Plant dataset
Connectivity constraints
Summary
Questions
Further reading
5
Soft Clustering and Gaussian Mixture Models
Soft Clustering and Gaussian Mixture Models
Technical requirements
Soft clustering
Fuzzy c-means
Gaussian mixture
Summary
Questions
Further reading
6
Anomaly Detection
Anomaly Detection
Technical requirements
Probability density functions
Histograms
Kernel density estimation (KDE)
Anomaly detection
One-class support vector machines
Anomaly detection with Isolation Forests
Summary
Questions
Further reading
7
Dimensionality Reduction and Component Analysis
Dimensionality Reduction and Component Analysis
Technical requirements
Principal Component Analysis (PCA)
Independent Component Analysis
Topic modeling with Latent Dirichlet Allocation
Summary
Questions
Further reading
8
Unsupervised Neural Network Models
Unsupervised Neural Network Models
Technical requirements
Autoencoders
Hebbian-based principal component analysis
Unsupervised deep belief networks
Summary
Questions
Further reading
9
Generative Adversarial Networks and SOMs
Generative Adversarial Networks and SOMs
Technical requirements
Generative adversarial networks
Self-organizing maps
Summary
Questions
Further reading
10
Assessments
Assessments
Chapter 1
Chapter 2
Chapter 3
Chapter 4
Chapter 5
Chapter 6
Chapter 7
Chapter 8
Chapter 9
11
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K-means

K-means is the simplest implementation of the principle of maximum separation and maximum internal cohesion. Let's suppose we have a dataset X ∈ ℜM×N (that is, M N-dimensional samples) that we want to split into K clusters and a set of K centroids corresponding to the means of the samples assigned to each cluster Kj:

The set M and the centroids have an additional index (as a superscript) indicating the iterative step. Starting from an initial guess M(0), K-means tries to minimize an objective function called inertia (that is, the total average intra-cluster distance between samples assigned to a cluster Kj and its centroid μj):

It's easy to understand that S(t) cannot be considered as an absolute measure because its value is highly influenced by the variance of the samples. However, S(t+1) < S(t) implies that the centroids are moving...

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