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What does sumd method in k-means clustering function exactly calculate?

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Onur Kapucu
Onur Kapucu 2018-5-8
评论： Onur Kapucu 2018-5-8
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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采纳的回答

Ameer Hamza
Ameer Hamza 2018-5-8
编辑：Ameer Hamza 2018-5-8
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)

更多回答（1 个）

the cyclist
the cyclist 2018-5-8
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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