K-means - evaluating the performance of clustering models

With models such as regression, classification, and recommendation engines, there are many evaluation metrics that can be applied to clustering models to analyze their performance and the goodness of the clustering of the data points. Clustering evaluation is generally divided into either internal or external evaluation. Internal evaluation refers to the case where the same data used to train the model is used for evaluation. External evaluation refers to using data external to the training data for evaluation purposes.

Internal evaluation metrics

Common internal evaluation metrics include the WCSS we covered earlier (which is exactly the k-means objective function), the Davies-Bouldin Index, the Dunn Index...