python-scikit-learnHierarchical clustering example
from sklearn import datasets, cluster
X, _ = datasets.load_iris(return_X_y=True)
model = cluster.AgglomerativeClustering(n_clusters = 3)
clusters = model.fit_predict(X)ctrl + cfrom sklearn importimport module from scikit-learn | load_irisloads Iris dataset |
.AgglomerativeClustering(create agglomerative clustering model (hierarchical clustering model) | n_clustersnumber of clusters we want to see |
.fit_predict(trains model and returns predicted cluster labels | |
Usage example
from sklearn import datasets, cluster
X, _ = datasets.load_iris(return_X_y=True)
model = cluster.AgglomerativeClustering(n_clusters = 3)
clusters = model.fit_predict(X)
print(clusters)output
[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 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 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 2 2 2 2 0 2 2 2 2
2 2 0 0 2 2 2 2 0 2 0 2 0 2 2 0 0 2 2 2 2 2 0 0 2 2 2 0 2 2 2 0 2 2 2 0 2
2 0]
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