k means - Normalising Data to use Cosine Distance in Kmeans (Python) -


i solving problem have use cosine distance similarity measure kmeans clustering. however, standard kmeans clustering package (from sklearn package) uses euclidean distance standard, , not allow change this.

therefor understanding normalising original dataset through the code below. can run kmeans package (using euclidean distance) , same if had changed distance metric cosine distance?

from sklearn import preprocessing  # normalise existing x x_norm = preprocessing.normalize(x)  km2 = cluster.kmeans(n_clusters=5,init='random').fit(x_norm)

please let me know if mathematical understanding of incorrect?


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