A clustering algorithm usually requires initialization before it could be started.
Seeding is a type of clustering initialization, which provides a few seeds -- points from a data set that would serve as the initial cluster centers (one for each cluster).
Each seeding algorithm implemented by Clustering.jl is a subtype of
SeedingAlgorithm:
SeedingAlgorithm
initseeds!
initseeds_by_costs!
There are several seeding methods described in the literature. Clustering.jl implements three popular ones:
KmppAlg
KmCentralityAlg
RandSeedAlg
In practice, we have found that Kmeans++ is the most effective choice.
For convenience, the package defines the two wrapper functions that accept
the short name of the seeding algorithm and the number of clusters and take
care of allocating iseeds and applying the proper SeedingAlgorithm:
initseeds
initseeds_by_costs