There are essentially two types of hierarchical cluster analysis strategies:
given a dataset (d 1 , d 2 , d 3 , .... d N ) of size N # compute the distance matrix for i = 1 to N: # as the distance matrix is symmetric about # the primary diagonal so we compute only lower # part of the primary diagonal for j = 1 to i: dis_mat [i] [j] = distance [d i , d j ] each data point is a singleton cluster repeat merge the two cluster having minimum distance update the distance matrix untill only a single cluster remains
Implementation of the above algorithm in Python using the scikit-learn library:
[1, 1, 1, 0, 0, 0]
given a dataset (d 1 , d 2 , d 3 , .... d N ) of size N at the top we have all data in one cluster the cluster is split using a flat clustering method eg. K-Means etc repeat choose the best cluster among all the clusters to split split that cluster by the flat clustering algorithm untill each data is in its own singleton cluster
Hierarchical agglomeration versus em> divisional clustering —
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