Clustering automatically groups the parts of a catalog into clusters based on their geometry and organizes them into a multi-level structure. Typical applications include:
Organizing accumulated inventories of in-houseparts. Catalogs of in-house parts often contain many thousands of parts without a useful classification system. The clustering process generates, in a single run, a folder structure sorted by similarity with descriptive labels.
Creating a new master catalog. The result is exported to a new catalog that completely replaces the existing inventory—every part from the original is included, even those that cannot be clustered.
Preliminary work for standardization and part reduction. Because geometrically similar parts are located immediately next to each other in the structure tree, duplicates, near-duplicates, and unnecessary variants are easily spotted during review. The clustering thus complements the duplicate analysis, but does not replace it.
Improved discoverability. Instead of folders organized by item numbers, clusters are created with nodes named according to subject matter, which can be navigated and searched.