Data Science Stack Exchange is a question and answer site for Data science professionals, Machine Learning specialists, and those interested in learning more about the field. It only takes a minute to sign up.
Sign up to join this community
We have a large amount (Billions) of high cardinality, mixed nominal & numerical data, and are performing some clustering on it as an experiment. There is a small subset of these data, however, that we know should belong in its own cluster.
Is it possible to purposely make sure these data end up in their own cluster, while still clustering the remaining data? If so, how does one approach doing so?
In constrained clustering you can provide examples of objects that should, or that must not, be in the same cluster.
This can be used, e.g., for model selection: run several times and return the result with the fewest violations. Or to guide cluster extraction from a hierarchical result.
