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.

Preference

Required string input.

Damping

Required number input.

Iterations

Required integer input.

Identify exemplar points through iterative responsibility and availability message passing. 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]

Clusters

The resulting clusters 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.

• K-Means Clustering Tool

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

• Mean Shift Clustering Tool

  Shift each point to the mean of points within a fixed Euclidean bandwidth, then merge nearby modes. Bandwidth controls the number of modes.

• OPTICS Clustering Tool

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