Use this analysis to represent observations through a small set of extreme but data-derived patterns and examine how each observation is composed from them.

Observations

Required list input.

Archetypes

Required integer input.

Select representative boundary observations with a deterministic farthest-first archetype initialization. The tool evaluates the supplied inputs together and returns the named outputs below; it does not infer omitted operating conditions or change the units shown.^[1]

Archetypes

The resulting archetypes returned as an object.

• The result depends on scaling, distance, missing-data treatment, initialization, and the selected number of archetypes. Archetypes summarize the supplied sample; they are not automatically causal, stable, or representative of unseen populations.
• Use finite inputs in the displayed units and preserve more precision than the final presentation requires. Independently verify safety-critical, financial, compliance, or production decisions.

Standardize features deliberately, compare several archetype counts and random starts, and inspect residuals and sensitivity. Compare with clustering or dimensionality reduction when the analytical question is about centers or variance instead of extremes.

• K-Means Clustering Tool

  Cluster numeric points by iterative nearest-centroid assignment and arithmetic centroid updates. Farthest-point deterministic initialization avoids random results.

• Vertex-Cover 2-Approximation Demonstrator

  Build a maximal matching and select both endpoints for an unweighted factor-2 vertex cover.

• Bloom Filter Calculator

  Build a deterministic Bloom filter, test an item, and estimate its false-positive probability.