Before revision: Some researchers have proposed using non-Euclidean metrics for clustering data points . Generally, the metric should recognize that two points in the same cluster are close , even if their Euclidean distance is far. Multiple proposals have been suggested, including the Edge-Squared Metric (a specific example of a graph geodesic) and the Nearest Neighbor Metric, a geodesic metric .
After revision: Some researchers have proposed using non-Euclidean metrics for clustering data points . Generally, the metric should recognize that two points in the same cluster are close , even if their Euclidean distance is long. This property is desirable in machine learning .