Stupid farthest neighbour random clustering
Picks k random starting points from given dataset to initialise k clusters. Then, one by one, a point not yet assigned to any cluster is assigned to that cluster, until all points are assigned. The point/cluster pair to be used is picked according to the smallest distance of a point to the farthest point to it in any of the already existing clusters as in complete linkage clustering, see Akhanli and Hennig (2020).
stupidkfn(d,k)
d |
|
k |
integer. Number of clusters. |
The clustering vector (values 1 to k, length number of objects
behind d),
Akhanli, S. and Hennig, C. (2020) Calibrating and aggregating cluster validity indexes for context-adapted comparison of clusterings. Statistics and Computing, 30, 1523-1544, https://link.springer.com/article/10.1007/s11222-020-09958-2, https://arxiv.org/abs/2002.01822
set.seed(20000) options(digits=3) face <- rFace(200,dMoNo=2,dNoEy=0,p=2) stupidkfn(dist(face),3)
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