封面
版权信息
Preface
1. Introduction to Clustering
Introduction
Unsupervised Learning versus Supervised Learning
Clustering
Introduction to k-means Clustering
Summary
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. Neighborhood Approaches and DBSCAN
Introduction
Clusters as Neighborhoods
Introduction to DBSCAN
DBSCAN versus k-means and Hierarchical Clustering
Summary
4. Dimensionality Reduction Techniques and PCA
Introduction
What Is Dimensionality Reduction?
Overview of Dimensionality Reduction Techniques
Principal Component Analysis
Summary
5. Autoencoders
Introduction
Fundamentals of Artificial Neural Networks
Autoencoders
Summary
6. t-Distributed Stochastic Neighbor Embedding
Introduction
The MNIST Dataset
Stochastic Neighbor Embedding (SNE)
t-Distributed SNE
Interpreting t-SNE Plots
Summary
7. Topic Modeling
Introduction
Topic Models
Cleaning Text Data
Latent Dirichlet Allocation
Non-Negative Matrix Factorization
Summary
8. Market Basket Analysis
Introduction
Market Basket Analysis
Characteristics of Transaction Data
The Apriori Algorithm
Association Rules
Summary
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
更新时间：2021-06-18 18:13:09
完结共72章
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