Matching music: clustering versus distinguishing records in a large database

Describing musical pieces, whether sound recordings, scores, librettos, videos, has always involved cataloger interpretation and judgment. There is considerable variation in records created for exactly the same item. And there is never “proof” that two records which seem to describe the same item actually do. This paper aims to address this issue.

This paper describes some of the challenges encountered in developing software for matching music records, and some approaches to making the software reliable.

The paper finds that matching can be used successfully to create GLIMIR clusters in the WorldCat database. Work is needed in several areas to complete the implementation, but intermediate results are promising.

This implementation will allow end‐user applications to collocate resources, to improve discovery and delivery in a complex bibliographic universe