Choosing K via Elbowpoint Method
December 08, 2019
Graph displaying an optimal cluster configuration at the elbow point
Often it is uncertain how many clusters is best to choose. In the elbow point method, you choose a different number of clusters and start plotting the within-cluster distance to the centroid.
From this graph we can infer that at k=4, the graph reaches an optimum minimum value. Even though the within-cluster distance decreases after 4, we would be doing more computations. Therefore, we choose a value of 4 as the optimum number of clusters.
Source: TowardsDataScience K-Means Clustering — Introduction to Machine Learning Algorithms Simplest clustering algorithm — Code & Explanation. Rohith Gandhi
Source URL: https://towardsdatascience.com/k-means-clustering-introduction-to-machine-learning-algorithms-c96bf0d5d57a
ID: choosing-k-via-elbowpoint-method
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