nearest-neighbor-chain

Corpus partitioning by semantic similarity is done by decomposing a pairwise similarity matrix into greedy nearest-neighbor chains. Sorting above-threshold pairs by descending score and extending chains at endpoints preserves topic boundaries for document consolidation.

Document consolidation from a similarity matrix uses greedy nearest-neighbor chaining to group related items. Applying a cutoff threshold prevents weak connections from over-merging, leaving ungrouped items as singletons while preserving semantic threads for downstream merging.

A similarity threshold controls topic segmentation by filtering weak connections. Pairs scoring below the threshold cutoff become singletons, ensuring only strong pairwise scores indicate adjacency and preventing the over-merging of unrelated documents during chain decomposition.

Endpoint extension builds nearest-neighbor semantic chains by adding items only at the ends of existing chains without merging separate chains. This greedy process walks pairs sorted by descending similarity score, creating ragged chains that act as semantic threads for cross-referencing.

Greedy chaining is used for cross-referencing when strong pairwise scores indicate adjacency and ordered topic threads are needed. Unlike broad clustering, it exposes topic boundaries and supports score-range outcomes like MERGE, migrate, or xref for downstream consolidation planning.

Remaining items become singletons after similarity matrix decomposition because they fall below the threshold cutoff for strong pairwise adjacency. These ungrouped items represent isolated topics that lack sufficient similarity scores to join any greedy nearest-neighbor chain.