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-# Workflow
-
-Separate problem in half, first find clusters, then examine clusters (as
-proposed).
-
-## Finding clusters
-
-* group by raw exact title
-* group by lowercase title
-* group by slug title
-* group by ngram title and authors
-* group by ngram title (prefix, suffix) and authors
-* group by elasticsearch
-* group by doi without vX prefix
-* group by soundex
-* group by a simhash over the record
-
-As for performance, the feature needs to be calculated in one pass, then the
-grouping reduces to a sort, in a second pass.
-
-The output could be a TSV file, with method and then release identifiers.
-
-```
-rawt o3utonw5qzhddo7l4lmwptgeey nnpmnwln7be2zb5hd2qanq3r7q
-```
-
-Or jsonlines for a bit of structure.
-
-```
-{"m": "rawt", "c": ["o3utonw5qzhddo7l4lmwptgeey", "nnpmnwln7be2zb5hd2qanq3r7q"]}
-```
-
-```
-$ zstdcat -T0 release_export_expanded.json.zst | fuzzycat-cluster -g > clusters.json
-```
-
-### Performance considerations
-
-* [orjson](https://github.com/ijl/orjson), [pysimdjson](https://github.com/TkTech/pysimdjson)
-
-
-## Examine cluster
-
-There will be various methods by which to examine the cluster as well.
-
-We need to fetch releases by identifier, this can be the full record or some
-partial record that has been cached somewhere.
-
-The input is then a list of releases and the output would be a equally sized or
-smaller cluster of releases which we assume represent the same record.
-
-Apart from that, there may be different relations, e.g. not the exact same
-thing, but something, that has an interval to it, like some thing that mostly
-differs in year?