HierarchicalClustering.py

from sklearn.datasets import make_blobs
from sklearn.cluster import AgglomerativeClustering
import matplotlib.pyplot as plt
from scipy.cluster.hierarchy import dendrogram,linkage


X,_=make_blobs(n_samples=300,centers=4,cluster_std=1,random_state=42)
# plt.figure()
# plt.scatter(X[:,0],X[:,1])


linkage_methods=["ward","single","average","complete"]

for i , linkage_method in enumerate(linkage_methods,1):
    model = AgglomerativeClustering(n_clusters=4,linkage=linkage_method)
    cluster_labels= model.fit_predict(X)

    plt.subplot(2,4,i)
    plt.title(f"{linkage_method.capitalize()}Linkage Dendrogram")
    dendrogram(linkage(X,method=linkage_method),no_labels=True)
    plt.xlabel("veri noktaları")
    plt.ylabel("uzaklık")
    plt.subplot(2,4,i+4)
    plt.scatter(X[:,0],X[:,1],c=cluster_labels,cmap="viridis")
    plt.title(f"{linkage_method.capitalize()}Linkage Clustering")
plt.show()