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.

Radius threshold

Required number input.

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

Microclusters

The resulting microclusters 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.

• OPTICS Clustering Tool

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

• Affinity Propagation Clustering Tool

  Identify exemplar points through iterative responsibility and availability message passing.

• Fuzzy C-Means Clustering Tool

  Fit fuzzy c-means memberships that sum to one across clusters for each point; fuzzifier must exceed one.