Initial centroids for K-means clustering
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If I have an array (i.e., 5 by 3 matrix) can serve as the initial centroids for kmeans clustering, how can I properly initialize the kmeans algorithm?
(Matlab's kmeans function has more than 600 lines of code and I have no idea how to modify it...)
The purpose of having my own initial centroids rather than have them randomly generated in the kmeans function is to remove the randomness in the outputs.
P.s. Python has the answer to it but I don't know Python.
1 Comment
Adam
on 17 Sep 2019
Edited: Adam
on 17 Sep 2019
You should always read the documentation before the code. The 'Start' option gives you the option to input your own initial cluster centres.
I always suggest using your embedded help though via
doc kmeans
and clicking on the 'Name','Value' hyperlink in the 2nd function signature to take you to the list of possible (Name,Value) pairs that are supported. If you always use the latest version of Matlab the online help is fine though.
Answers (1)
KALYAN ACHARJYA
on 17 Sep 2019
Edited: KALYAN ACHARJYA
on 17 Sep 2019
Before I share the helpful link, I requested you to watch the Andrew Ng. lecture on Random Initialization of K menas (Machine Learning).
He suggests to avoid k-means stuck in local minima or ensure the optimize K-menas, choose multiple random initailizations.
Manual Initialization
2 Comments
Adam
on 17 Sep 2019
As I added in a comment above, the Matlab help is always the first place to go. This shows how you can do this.
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