CODEWITHSUNDEEP
cluster-analysiscosine-similarity
nvayien iaziz
nvayien iaziz

Reputation: 1

Kmean Algorithm and Cosine distance

I have used K-mean Algorithm with euclidean distance to cluster my dataset, then i tried cosine distance, but the algorithm does not converge with cosine metrics (it is not stopping - iteration reach to 1000 )

any suggestion please

Upvotes: 0

Views: 434

Answers (1)

Has QUIT--Anony-Mousse
Has QUIT--Anony-Mousse

Reputation: 77505

Cosine should be okay, because it is equivalent to Euclidean on L2 normalized data. Do unless you have a programming error, it should converge.

So alternatively, you could L2 normalize your data, then use regular k-means instead og spherical k-means.

Note thst Mini-Batch by design doesn't converge, in case you are using that. If you sample batches, it cannot.

Upvotes: 0

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