clustering evaluation metrics

Clustering evaluation metrics measure how well a grouping algorithm organizes data. External metrics compare the clusters to known labels, assessing accuracy and consistency with real categories. Internal metrics analyze the data itself, checking how tightly grouped the points are and how well separate the clusters are from each other. For example, some metrics look for clusters with similar points, while others check that clusters are distinct. These metrics help determine if the resulting clusters are meaningful and useful for understanding data patterns, guiding improvements in clustering methods.