find optimal clustering number using silhouette evaluation

 

To Find optimal clustering number using silhouette metrics

It evaluate clustering resulting in every k number of KMean algorithm.

And show it as figure.

Lager value is better result.

..

from sklearn.cluster import KMeans

from sklearn.metrics import silhouette_samples

import numpy as np

import matplotlib.pyplot as plt

silhouette_vals = []

sk,ek = 2,20

for i in range(sk, ek):

kmeans_plus = KMeans(n_clusters=i, init='k-means++')

pred = kmeans_plus.fit_predict(cluster_df)

silhouette_vals.append(np.mean(silhouette_samples(cluster_df, pred, metric='euclidean')))

plt.plot(range(sk, ek), silhouette_vals, marker='o')

plt.xlabel('Number of clusters')

plt.ylabel('Silhouette')

plt.show()

..

For example here, 20 k is best clustering result.

Thank you.