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Chapter 10
Figure 4: Plot of the final centroids after 100 iterations
Please note that the plot command works in Matplotlib 3.1.1 or
higher versions.
In the preceding code we decided to limit the number of clusters to 3, but in most
cases with unlabeled data, one is never sure how many clusters exist. One can
determine the optimal number of clusters using the elbow method. The method
is based on the principle that we should choose the cluster number that reduces
the sum of squared error (SSE) distance. If k is the number of clusters, then as k
increases, the SSE decreases, with SSE = 0; when k is equal to the number of data
points, each point is its own cluster. We want a low value of k, such that SSE is also
low. For the famous Fisher's Iris data set, if we plot SSE for different k values, we can
see from the plot below that for k=3, the variance in SSE is the highest; after that, it
starts reducing, thus the elbow point is k=3:
Figure 5: Plotting SSE against the Number of Clusters, using Fisher's Iris data set
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