K-means clustering algorithm

How to set k in k-means clustering
For K=1, 2, 3, …, run the k-means clustering algorithm.
After the k-means algorithm has converged, we have cluster assignments for each
sample as well as the locations of the cluster centers.
Compute
as d
K
Let d
K
Let the
1
N
be
the mean squared distance
K
j
1
( i )
0 x Cluster j
the distortion
x
of
( i)
transformed distortion
m
j
2
the clustering
be d
.
K
of
p / 2
a sample
result.
from its
, where p is the dimension
corresponding
of
cluster center
d K decreases as K increases
the data samples.
The jump value of transformed distortion
(Assume d
0
0 when computing
J
1
.)
is
J
K
d
p / 2
K
d
p / 2
K 1
.
The peak of the jump values corresponds to the K that provides the best description of
the original samples.