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Ferdi
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I have to classify some data without any futher prediction (I just need the best clusters on the data). Do I still have to train-test-split my data or do a kfoldCV?

And how do I evaluate my clustering algorithmes (kmeans, spectral, KNN, SOM etc..)? (I already looked the score function of my algorithmes, the KL, the silhouette, inter-intra classes distances)

I think, doing statistic tests would be meaningless, because I don't split the data.

Can you answer those questions?

Many thanks

I have to classify some data without any futher prediction (I just need the best clusters on the data). Do I still have to train-test-split my data or do a kfoldCV?

And how do I evaluate my clustering algorithmes (kmeans, spectral, KNN, SOM etc..)? (I already looked the score function of my algorithmes, the KL, the silhouette, inter-intra classes distances)

I think, doing statistic tests would be meaningless, because I don't split the data.

Can you answer those questions?

Many thanks

I have to classify some data without any futher prediction (I just need the best clusters on the data). Do I still have to train-test-split my data or do a kfoldCV?

And how do I evaluate my clustering algorithmes (kmeans, spectral, KNN, SOM etc..)? (I already looked the score function of my algorithmes, the KL, the silhouette, inter-intra classes distances)

I think, doing statistic tests would be meaningless, because I don't split the data.

Can you answer those questions?

Source Link

Clustering without test set and evaluation

I have to classify some data without any futher prediction (I just need the best clusters on the data). Do I still have to train-test-split my data or do a kfoldCV?

And how do I evaluate my clustering algorithmes (kmeans, spectral, KNN, SOM etc..)? (I already looked the score function of my algorithmes, the KL, the silhouette, inter-intra classes distances)

I think, doing statistic tests would be meaningless, because I don't split the data.

Can you answer those questions?

Many thanks