Silhouette coefficient
The silhouette coefficient measures how well a sample fits its assigned cluster compared with the nearest alternative cluster, typically ranging from -1 to 1.
Statistics Slam Dunk
A silhouette coefficient measures an observation's relative cohesion within its assigned cluster and separation from the nearest other cluster, typically ranging from -1 to +1.
- How it is used
- Selects an appropriate number of clusters when the elbow plot is subjective or lacks a clear bend.
- How deep it goes
- Defines and derives the coefficient from dissimilarities, then uses plotted values to choose K without discussing implementation internals.
- Examples
- Evaluates NBA team clusters formed from standardized payroll and regular-season wins.
- What you can do after
- Apply it via factoextra's fviz_nbclust() to compare candidate cluster counts in R.
Read how Statistics Slam Dunk explains silhouette coefficient →
Cyber Threat Hunting
The silhouette coefficient is a clustering metric that compares an observation's cohesion within its assigned cluster with its separation from neighboring clusters, ranging from -1 to 1.
- How it is used
- Uses cohesion and separation to choose or validate k in k-means clustering.
- How deep it goes
- Defines per-point distances, gives the formula and range, and explains computational cost without deriving distance implementation.
- Examples
- Demonstrates k ranges 2–10 and 2–12 with plots, timing comparisons, and a k = 10 cluster-size countplot.
- What you can do after
- Evaluate candidate cluster counts with KElbowVisualizer and compare silhouette analysis against the elbow method.
Read how Cyber Threat Hunting explains silhouette coefficient →
Related concepts
- elbow methodalternative to · The elbow method is a heuristic for selecting model complexity or a parameter value by locating where increasing it yields diminishing improvement in an evaluation criterion.
- intra-cluster distanceuses · Intra-cluster distance is a measure of dissimilarity among observations within the same cluster or between them and a representative point.
- clusteringmeasures · Clustering is the technique of grouping data items according to similarity or shared characteristics.
- metrickind of · A metric is a quantitative measure used to characterize or assess a property, condition, behavior, performance, or outcome.
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