ClusteringAlgorithmComparison.py

from sklearn import datasets, cluster
from sklearn.preprocessing import StandardScaler
import numpy as np
import matplotlib.pyplot as plt

n_samples= 1500

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]

i=1
plt.figure()
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(),s=10)


        i+=1
plt.show()