Use this clustering method to explore structure in numeric observations after selecting a meaningful feature representation, distance or similarity measure, and algorithm-specific controls.

Points

Required list input.

Maximum radius

Required number input.

Minimum points

Required integer input.

Order multidimensional points by density reachability with the OPTICS clustering algorithm. 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]

OPTICS ordering

The resulting optics ordering returned as an object.

• Clusters are model-dependent rather than ground truth. Scaling, outliers, dimensionality, initialization, stopping criteria, density variation, and hyperparameters can materially change the partition.
• 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 when appropriate, repeat stochastic runs, inspect cluster stability and diagnostics, and compare another algorithm whose assumptions differ. Validate usefulness against the downstream question rather than cluster count alone.

• Affinity Propagation Clustering Tool

  Identify exemplar points through iterative responsibility and availability message passing.

• BIRCH Microclustering Demonstrator

  Build order-dependent BIRCH-style microclusters using a maximum-radius threshold.

• K-Means Clustering Tool

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