Meta-Learning For Better Machine Learning
Rocket-Powered Data Science
DECEMBER 16, 2018
So, you start by assuming a value for k and making random assumptions about the cluster means, and then iterate until you find the optimal set of clusters, based upon some evaluation metric. There are several choices for such evaluation metrics: Dunn index, Davies-Bouldin index, C-index, and Silhouette analysis are just a few examples.
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