<p>there is an -init option for that </p>

<p><em>Initialization method to use.
  0 = random, 1 = k-means++, 2 = canopy, 3 = farthest first.
  (default = 0)</em>
kmeans++ will give you an option for you to initialize centres.</p>

CC BY-SA 4.0
2018-06-02T13:29:06.447Z
2018-06-02T13:29:06.447Z
thecomplexitytheorist
<p>Is there a way in the WEKA explorer to manually select the initial centres when using SimpleKMeans clustering?</p>

CC BY-SA 4.0
2018-05-31T16:38:57.007Z
why would you want to choost your centres "manually" , they are assigned at random locations and they are re-assigned after each iteration of the clustering algorithm , so there is no point in "manually" initializing the centres.
thecomplexitytheorist
CC BY-SA 4.0
2018-06-02T12:54:17.880Z
I understand that is how they are usually selected. However, I was told I had to set the initial centres for an assignment and could not find a way to do so.
Hogg44
<machine-learning>
WEKA - SimpleKMeans - Manually choose intitial centres
Hogg44
# WEKA - SimpleKMeans - Manually choose intitial centres

(Asked by: username_0 on 2018-05-26)

Is there a way in the WEKA explorer to manually select the initial centres when using SimpleKMeans clustering?

**Tags:** machine-learning

> **Comments:**
> **username_1**: why would you want to choost your centres "manually" , they are assigned at random locations and they are re-assigned after each iteration of the clustering algorithm , so there is no point in "manually" initializing the centres.  
> **username_0**: I understand that is how they are usually selected. However, I was told I had to set the initial centres for an assignment and could not find a way to do so.  

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## 1 answers

### Answer by username_1 on 2018-06-02. Score: 1

there is an -init option for that

*Initialization method to use.
0 = random, 1 = k-means++, 2 = canopy, 3 = farthest first.
(default = 0)*
kmeans++ will give you an option for you to initialize centres.


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