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Partitioning clustering (k-means, k-kernel, etc.). Minimum k=2.

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Partitioning clustering (k-means, k-kernel, etc.). Minimum k=2.

[ Edited ]

Good day,

 

Why the for the partitioning clustering algorithms the minimum input parameter k=2 ? If i want to change it to 1, it's automatically set it to 2 (nota bene k-means and others are applicable for k=1...K). How then to know if the data set contains one cluster (without validation)?

 

Best regards,

TP

1 REPLY
Community Manager

Re: Partitioning clustering (k-means, k-kernel, etc.). Minimum k=2.

K=1 means you get one cluster, so that won't work. The default is K=2, for two clusters.

 

If you want to find the optimal # of clusters, use the X-means operator. The defaults are a min of 2 and a max to 60.

Regards,
Thomas
LinkedIn: Thomas Ott
Blog: Neural Market Trends