Import matplotlib.cm as cm import matplotlib.pyplot as plt import numpy as np from sklearn.cluster import KMeans from sklearn.datasets import make_blobs from trics import silhouette_samples, silhouette_score # Generating the sample data from make_blobs # This particular setting has one distinct cluster and 3 clusters placed close # together. Verified from the labelled scatter plot on the right. Or less of similar thickness and hence are of similar sizes as can be also However when the n_clusters is equal to 4, all the plots are more The silhouette plot for cluster 0 when n_clusters is equal toĢ, is bigger in size owing to the grouping of the 3 sub clusters into one bigĬluster. Silhouette analysis is more ambivalent in decidingĪlso from the thickness of the silhouette plot the cluster size can be The silhouette plot shows that the n_clusters value of 3, 5Īnd 6 are a bad pick for the given data due to the presence of clusters withīelow average silhouette scores and also due to wide fluctuations in the size In this example the silhouette analysis is used to choose an optimal value for Two neighboring clusters and negative values indicate that those samples might Indicates that the sample is on or very close to the decision boundary between That the sample is far away from the neighboring clusters. Silhouette coefficients (as these values are referred to as) near +1 indicate Point in one cluster is to points in the neighboring clusters and thus providesĪ way to assess parameters like number of clusters visually. The silhouette plot displays a measure of how close each Silhouette analysis can be used to study the separation distance between the To download the full example code or to run this example in your browser via JupyterLite or Binder Selecting the number of clusters with silhouette analysis on KMeans clustering ¶
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