Difference between CURE Clustering and DBSCAN Clustering
Clustering is a technique used in Unsupervised learning in which data samples are grouped into clusters on the basis of similarity in the inherent properties of the data sample. Clustering can also be defined as a technique of clubbing data items that are similar in some way. The data items belonging to the same clusters are similar to each other in some way while the data items belonging to different clusters are dissimilar.
CURE (Clustering Using Representatives) and DBSCAN (Density Based Spatial Clustering of Applications with Noise) are clustering algorithms used in unsupervised learning. CURE is a hierarchial based clustering technique and DBSCAN is a densitybased clustering technique.
These are some differences between CURE and DBSCAN :
S.No.  CURE Clustering  DBSCAN Clustering 

1.  CURE Clustering stands for Clustering Using Representatives Clustering.  DBSCAN Clustering stands for Density Based Spatial Clustering of Applications with Noise Clustering. 
2.  It is a hierarchial based clustering technique.  It is a density based clustering technique. 
3.  Noise handling in CURE clustering is not efficient.  Noise handling in DBSCAN clustering is efficient. 
4.  Algorithm:

Algorithm:

5.  It can take care of high dimensional datasets.  It does not work properly for high dimensional datasets. 
6.  Varying densities of the data points doesn’t matter in CURE clustering algorithm.  It does not work properly when the data points have varying densities 
CURE Architecture:
DBSCAN Architecture:
Eps : Radius of circle
minPts : It is the minimum no. of points that must exist in the vicinity of eps.
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