1. CURE uses a hierarchical clustering method that selects a middle ground between the centroid based and all point extremes in order to avoid the issues with non-uniformly sized or formed clusters.
2. In CURE, a fixed number c of evenly spaced-out points from a cluster are selected, and they are shrunk by a fraction in the direction of the cluster centroid.
3. The scattered points that have shrunk are used as cluster representatives.
4. At each stage of the hierarchical clustering algorithm used by CURE, the clusters with the closest pair of representatives are the clusters that are combined.
5. This makes CURE less sensitive to outliers and allows it to appropriately detect the clusters.