What does sumd method in k-means clustering function exactly calculate?
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I am doing basic experiments with kmeans function. As a real simple example, say that I have a data set of 4 items with 1 attribute and this attribute is their value:
Data=[1;2;3;4];
If I want to split this data set into 2 clusters I should get one centroid in 1.5 and another in 3.5:
[idx,C,sumd]=kmeans(Data,2)
C =
1.5000
3.5000
and I get it. However to my understanding sumd in this case should be:
abs(1-1.5)+abs(2-1.5) or abs(3-3.5)+abs(4-3.5)
ans =
1
but I am getting sumd as:
sumd =
0.5000
0.5000
for both clusters. Instead of getting 1's for both.
My question is what exactly does sumd calculate?
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Ameer Hamza
le 8 Mai 2018
Modifié(e) : Ameer Hamza
le 8 Mai 2018
If you look at the documentation of kmeans(), you will know that it uses the square of the Euclidean distance, by default. So you should calculate it like this
abs(1-1.5).^2+abs(2-1.5).^2 or abs(3-3.5).^2+abs(4-3.5).^2
ans =
0.5 (both cases)
Plus de réponses (1)
the cyclist
le 8 Mai 2018
It's because the default distance metric used is the squared Euclidean distance (for minimization, and reporting). See the Distance input parameter.
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