更新时间：2021-07-02 12:32:34
coverpage
Title Page
Copyright and Credits
Hands-On Unsupervised Learning with Python
About Packt
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Packt.com
Contributors
About the author
About the reviewer
Packt is searching for authors like you
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
Conventions used
Get in touch
Reviews
Getting Started with Unsupervised Learning
Technical requirements
Why do we need machine learning?
Descriptive analysis
Diagnostic analysis
Predictive analysis
Prescriptive analysis
Types of machine learning algorithm
Supervised learning algorithms
Supervised hello world!
Unsupervised learning algorithms
Cluster analysis
Generative models
Association rules
Unsupervised hello world!
Semi-supervised learning algorithms
Reinforcement learning algorithms
Why Python for data science and machine learning?
Summary
Questions
Further reading
Clustering Fundamentals
Introduction to clustering
Distance functions
K-means
K-means++
Analysis of the Breast Cancer Wisconsin dataset
Evaluation metrics
Minimizing the inertia
Silhouette score
Completeness score
Homogeneity score
A trade-off between homogeneity and completeness using the V-measure
Adjusted Mutual Information (AMI) score
Adjusted Rand score
Contingency matrix
K-Nearest Neighbors
Vector Quantization
Advanced Clustering
Spectral clustering
Mean shift
DBSCAN
Calinski-Harabasz score
Analysis of the Absenteeism at Work dataset using DBSCAN
Cluster instability as a performance metric
K-medoids
Online clustering
Mini-batch K-means
BIRCH
Comparison between mini-batch K-means and BIRCH
Hierarchical Clustering in Action
Cluster hierarchies
Agglomerative clustering
Single and complete linkages
Average linkage
Ward's linkage
Analyzing a dendrogram
Cophenetic correlation as a performance metric
Agglomerative clustering on the Water Treatment Plant dataset
Connectivity constraints
Soft Clustering and Gaussian Mixture Models
Soft clustering
Fuzzy c-means
Gaussian mixture
EM algorithm for Gaussian mixtures
Assessing the performance of a Gaussian mixture with AIC and BIC
Component selection using Bayesian Gaussian mixture
