To avoid the problems with non-uniform sized or shaped clusters, CURE employs a hierarchical clustering algorithm that adopts a middle ground between the centroid based and all point extremes. In CURE, a constant number c of well scattered points of a cluster are chosen and they are shrunk towards the centroid of the cluster by a fraction α. The scattered points after shrinking are used as representatives of the cluster. The clusters with the closest pair of representatives are the clusters that are merged at each step of CURE's hierarchical clustering algorithm. This enables CURE to correctly identify the clusters and makes it less sensitive to outliers.
List down few details about CURE algorithm from given text
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.