sklearn
class sklearn.neighbors.KNeighborsClassifier(n_neighbors=5, weights='uniform', algorithm='auto', leaf_size=30, p=2, metric='minkowski', metric_params=None, n_jobs=1, **kwargs)
sklearn document
from sklearn.metrics import silhouette_samples, silhouette_score#The silhouette_score gives the average value for all the samples.This gives a perspective into the density and separation of the formed clusterssilhouette_avg = silhouette_score(X, cluster_labels)#Compute the silhouette scores for each samplesample_silhouette_values = silhouette_samples(X, cluster_labels)Finding the K in K-Means Clustering
Using BIC to estimate the number of k in KMEANS
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