Machine Learning / 14
Clustering
Find structure without labels. This section outlines the methods, distance choices, and validation questions to explore before interpreting a cluster.
Clusters are easy to draw. The hard part is deciding whether they mean anything.
This is a knowledge index. Published titles open their articles; the remaining entries outline planned coverage.
Subtopics
- 14.01
What is Clustering?
Planned guide: What is Clustering. Focus on intuition, assumptions, and a worked example.
- 14.02
Distance in Clustering
Planned guide: Distance in Clustering. Focus on intuition, assumptions, and a worked example.
- 14.03
K-Means
Plan a worked example of K-Means, including initialization, assignment, and interpretation.
- 14.04
K-Means Initialization
Planned guide: K-Means Initialization. Focus on intuition, assumptions, and a worked example.
- 14.05
Choosing K
Planned guide: Choosing K. Focus on intuition, assumptions, and a worked example.
- 14.06
Elbow Method
Planned guide: Elbow Method. Focus on intuition, assumptions, and a worked example.
- 14.07
Silhouette Score
Use a planned example to examine cluster separation and the limitations of a single score.
- 14.08
Hierarchical Clustering
Planned guide: Hierarchical Clustering. Focus on intuition, assumptions, and a worked example.
- 14.09
Agglomerative Clustering
Planned guide: Agglomerative Clustering. Focus on intuition, assumptions, and a worked example.
- 14.10
Divisive Clustering
Planned guide: Divisive Clustering. Focus on intuition, assumptions, and a worked example.
- 14.11
Dendrograms
Planned guide: Dendrograms. Focus on intuition, assumptions, and a worked example.
- 14.12
DBSCAN
Planned guide: DBSCAN. Focus on intuition, assumptions, and a worked example.
- 14.13
Density-Based Clustering
Planned guide: Density-Based Clustering. Focus on intuition, assumptions, and a worked example.
- 14.14
Gaussian Mixture Models
Planned guide: Gaussian Mixture Models. Focus on intuition, assumptions, and a worked example.
- 14.15
Soft Clustering
Planned guide: Soft Clustering. Focus on intuition, assumptions, and a worked example.
- 14.16
Cluster Validation
Planned guide: Cluster Validation. Focus on intuition, assumptions, and a worked example.
- 14.17
Cluster Assumptions
Planned guide: Cluster Assumptions. Focus on intuition, assumptions, and a worked example.
- 14.18
Choosing the Right Clustering Algorithm
Planned guide: Choosing the Right Clustering Algorithm. Focus on intuition, assumptions, and a worked example.
- 14.19
Clustering Failure Modes
Investigate unstable or misleading groupings before treating clusters as meaningful categories.