Clustering Metrics
Clustering Metrics groups the core ideas a learner needs at the 9 · evaluation, metrics & diagnostics stage. Work through the lessons in order when new to the area, or use them independently as a reference when implementing an analysis.
Learn the mechanism one decision at a time
Work through the lessons in order if the topic is new. If you already know the basics, open the specific leaf lesson that matches the operation, diagnostic or failure mode you need.
1Definition→
2Mechanism→
3Example→
4Diagnostic→
5Decision
Silhouette scoreSilhouette score is a practical concept within Clustering Metrics. It helps turn the broader workflow stage “9 · Evaluation, Metrics & Diagnostics” into an explicit analytical decision that can be explained, implemented and checked. The concept should be understood in terms of purpose, mechanism, assumptions, evidence and downstream consequences.
02Davies-Bouldin indexDavies-Bouldin index is a practical concept within Clustering Metrics. It helps turn the broader workflow stage “9 · Evaluation, Metrics & Diagnostics” into an explicit analytical decision that can be explained, implemented and checked. The concept should be understood in terms of purpose, mechanism, assumptions, evidence and downstream consequences.
03Calinski-Harabasz indexCalinski-Harabasz index is a practical concept within Clustering Metrics. It helps turn the broader workflow stage “9 · Evaluation, Metrics & Diagnostics” into an explicit analytical decision that can be explained, implemented and checked. The concept should be understood in terms of purpose, mechanism, assumptions, evidence and downstream consequences.
04Adjusted Rand IndexAdjusted Rand Index is a practical concept within Clustering Metrics. It helps turn the broader workflow stage “9 · Evaluation, Metrics & Diagnostics” into an explicit analytical decision that can be explained, implemented and checked. The concept should be understood in terms of purpose, mechanism, assumptions, evidence and downstream consequences.
05Normalized mutual informationNormalized mutual information is a practical concept within Clustering Metrics. It helps turn the broader workflow stage “9 · Evaluation, Metrics & Diagnostics” into an explicit analytical decision that can be explained, implemented and checked. The concept should be understood in terms of purpose, mechanism, assumptions, evidence and downstream consequences.
06Cluster stabilityCluster stability is a practical concept within Clustering Metrics. It helps turn the broader workflow stage “9 · Evaluation, Metrics & Diagnostics” into an explicit analytical decision that can be explained, implemented and checked. The concept should be understood in terms of purpose, mechanism, assumptions, evidence and downstream consequences.