If there are wider fluctuations like the following, the number of cluster is sub-optimal. Whether there is wide fluctuations in the size of the cluster plots.K-means clusters Silhouette Plot for n_clusters = 4 (Below Avg Score)
Thus, the choice of n_clusters = 4 will be sub-optimal.įig 3. If the silhouette plot for one of the clusters fall below the average Silhouette score, one can reject those numbers of clusters. Whether all the clusters’ Silhouette plot falls beyond the average Silhouette score.The Silhouette plots shown below have been created on the Sklearn IRIS dataset. Silhouette analysis/scores and related Silhouette plots look to have an edge over elbow method as one can evaluate clusters on multiple criteria such as the following and it is highly likely that one can end up determining the most optimal number of clusters in K-means. It is the point, from where the decrease in SSE starts looking linear. In the Elbow method where an SSE line plot is drawn, if the line chart looks like an arm, then the “elbow” on the arm is the value of k that is the best. The calculation simplicity of elbow makes it more suited than silhouette score for datasets with smaller size or time complexity. The major difference between elbow and silhouette scores is that elbow only calculates the euclidean distance whereas silhouette takes into account variables such as variance, skewness, high-low differences, etc. While both provide valuable information for clustering analysis elbow method is easy to implement and provides valuable results. It may be a good idea to use both plots just to make sure that you select the most optimal number of clusters. Both the Elbow method / SSE Plot and the Silhouette method can be used interchangeably based on the details presented by the plots. While the elbow method and silhouette score provide information on different aspects, both provide valuable information for clustering analysis. Silhouette plots for n_clusters = 2 to n_clusters = 7 Which technique to use – Elbow method vs Silhouette Score Here is how the Silhouette plot would look like for different numbers of clusters ranging from 2 to 7 clusters. Visualizer = SilhouetteVisualizer(km, colors='yellowbrick', ax=ax) Km = KMeans(n_clusters=i, init='k-means++', n_init=10, max_iter=100, random_state=42)Ĭreate SilhouetteVisualizer instance with KMeans instance
#Wmma 5 silhouette renders code
Here is the Python code using YellowBricks library for Silhouette analysis/plots:įrom yellowbrick.cluster import SilhouetteVisualizerįig, ax = plt.subplots(3, 2, figsize=(15,8))Ĭreate KMeans instance for different number of clusters It provides information about clustering quality which can be used to determine whether further refinement by clustering should be performed on the current clustering. The silhouette score of a point measures how close that point lies to its nearest neighbor points, across all clusters. SSE Plot / Elbow Method for finding optimal number of clusters As per the plot given below, for n_clusters = 4 that represents the elbow you start seeing diminishing returns by increasing k. Here is how the Elbow / SSE Plot would look like. Visualizer.show() # Finalize and render the figure Visualizer.fit(X) # Fit the data to the visualizer Visualizer = KElbowVisualizer(km, k=(2,10)) # Instantiate the clustering model and visualizer