N
objects into M
clusters, where no overlap is allowed.
Similar items are in a cluster and the cluster may be represented by a centroid or cluster representative that is indicative of the characteristics of the items it contains.
D1
as the representative for C1
.
Di
, calculate the similarity S
with the representative for each existing cluster.
Smax
is greater than a threshold value St
, add the item to the corresponding cluster and recalculate the cluster representative; otherwise, use Di
to initiate a new cluster.
Di
remains to be clustered, return to Step 2.
N-1
pairwise joins beginning from an unclustered data set, or divisive, beginning with all objects in a single cluster and progressing through N-1
divisions of some cluster into a smaller cluster.
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