python-scikit-learnUsing dbscan clustering example

from sklearn import datasets, cluster

X, y = datasets.load_iris(return_X_y=True)
model = cluster.DBSCAN()

clusters = model.fit_predict(X)ctrl + cgithub

`from sklearn import` import module from scikit-learn	`load_iris` loads Iris dataset
`.DBSCAN(` creates DBSCAN model	`.fit_predict(` trains model and returns predicted cluster labels

Usage example

from sklearn import datasets, cluster

X, y = datasets.load_iris(return_X_y=True)
model = cluster.DBSCAN()

clusters = model.fit_predict(X)
print(clusters)

output

[ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 -1  0  0  0  0  0  0
  0  0  1  1  1  1  1  1  1 -1  1  1 -1  1  1  1  1  1  1  1 -1  1  1  1
  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1 -1  1  1  1  1  1 -1  1  1
  1  1 -1  1  1  1  1  1  1 -1 -1  1 -1 -1  1  1  1  1  1  1  1 -1 -1  1
  1  1 -1  1  1  1  1  1  1  1  1 -1  1  1 -1 -1  1  1  1  1  1  1  1  1
  1  1  1  1  1  1]

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