Data Science
31
Clustering
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Clustering
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Table of contents
31.1
Learning objectives
31.2
The clustering question
31.3
Features, scaling, and distances
31.4
K-means
31.5
Hierarchical clustering
31.6
Assessing a clustering
31.7
Demo: Clustering with rtemis
31.8
Hands-on practice
31.9
Checkpoint
31.10
Further study
Data Science
31
Clustering
31
Clustering
31.1
Learning objectives
Frame a clustering question and select relevant features.
Explain the roles of distance, scaling, and algorithm choice.
Interpret a grouping while acknowledging its limitations.
31.2
The clustering question
31.3
Features, scaling, and distances
31.4
K-means
31.5
Hierarchical clustering
31.6
Assessing a clustering
31.7
Demo: Clustering with rtemis
31.8
Hands-on practice
31.9
Checkpoint
31.10
Further study
30
Introduction to Machine Learning
32
Decomposition and Dimensionality Reduction