Hierarchical Dissimilarity Clustering

How to use Hierarchical Dissimilarity Clustering

Merge the closest pair under the selected cross-cluster definition, retaining original membership and exact distances. Equal candidates follow a documented source-index tie rule; the final report includes the hierarchy and cophenetic matrix.

1. Use the example as the schema; replace its labels and entered model assumptions.
2. Merge the closest pair under the selected cross-cluster definition, retaining original membership and exact distances. Equal candidates follow a documented source-index tie rule; the final report includes the hierarchy and cophenetic matrix.
3. Calculate, inspect the complete witnesses and limitations, then copy or download the JSON report.

Good to know

• 2–40 distinct labels, an exact symmetric nonnegative decimal-string matrix with zero diagonal, and a cut from 1 through the label count. Single, complete and UPGMA average are the only modes.
• No Ward/centroid assumption, inferred coordinates, metric-learning, best cluster number or ground-truth group claim. Average uses every original cross pair, not an equal average of unequal child clusters.
• Local calculation with no upload, input persistence or outside lookup. Model JSON is limited to 30,000 characters and 10,000 values. Decimal quantities are strings with up to six places; count fields use plain JSON integers. Exact fractions are capped at 8,192 bits. The complete JSON output is limited to 1,000,000 characters; approximate numbers preserve their stored values. Unsupported states fail explicitly.