clusterscompareing.py

# -*- coding: utf-8 -*-
"""
Created on Thu Jul 31 15:28:32 2025

@author: asus
"""

from sklearn import  datasets,cluster
from sklearn.preprocessing import StandardScaler 
import numpy as np
import matplotlib.pyplot as plt
n_samples=1000
noisy_circles=datasets.make_circles(n_samples=n_samples,factor=0.5,noise=0.05)
noisy_moons=datasets.make_moons(n_samples=n_samples, noise=0.05)
blobs=datasets.make_blobs(n_samples=n_samples)
no_structure=np.random.rand(n_samples,2),None
clustering_names=["MiniBatchKMeans","SpectralClustering","Ward",
                  "AgglomerativeClustering","DBSCAN","Birch"]
colors=np.array(["b","g","r","c","m","y"])
datasets=[noisy_circles,noisy_moons,blobs,no_structure]
plt.figure()
i=1
for i_dataset,dataset in enumerate(datasets):
    X,y=dataset
    X=StandardScaler().fit_transform(X)
    two_means= cluster.MiniBatchKMeans(n_clusters=2)
    ward=cluster.AgglomerativeClustering(n_clusters=2,linkage="ward")
    spectral=cluster.SpectralClustering(n_clusters=2)
    dbscan=cluster.DBSCAN(eps=0.2)
    average_linkage=cluster.AgglomerativeClustering(n_clusters=2,linkage="average")
    birch=cluster.Birch(n_clusters=2)
    
    clustering_algorithms=[two_means,ward,spectral,dbscan,average_linkage,birch]
    
    for name,algo in zip(clustering_names,clustering_algorithms):
        
        algo.fit(X)
        
        if hasattr (algo,"labels_"):
            y_pred=algo.labels_.astype(int)
        else:
            y_pred=algo.predict(X)
        plt.subplot(len(datasets),len(clustering_algorithms),i)
        
        if i_dataset==0:
            plt.title(name)
        plt.scatter(X[:,0],X[:,1],color=colors[y_pred].tolist())
            
        i+=1