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.
Recommended Posts:
 DBSCAN Clustering in ML  Density based clustering
 Difference between KMeans and DBScan Clustering
 DBScan Clustering in R Programming
 Basic Understanding of CURE Algorithm
 ML  DBSCAN reachability and connectivity
 ML  Hierarchical clustering (Agglomerative and Divisive clustering)
 Implementing DBSCAN algorithm using Sklearn
 DBSCAN Full Form
 Difference between Hierarchical and Non Hierarchical Clustering
 Difference between Classification and Clustering in DBMS
 Difference between K means and Hierarchical Clustering
 K means Clustering  Introduction
 Clustering in R Programming
 Analysis of test data using KMeans Clustering in Python
 Clustering in Machine Learning
 Different Types of Clustering Algorithm
 ML  Unsupervised Face Clustering Pipeline
 ML  Determine the optimal value of K in KMeans Clustering
 ML  Mini Batch Kmeans clustering algorithm
 Image compression using Kmeans clustering
If you like GeeksforGeeks and would like to contribute, you can also write an article using contribute.geeksforgeeks.org or mail your article to contribute@geeksforgeeks.org. See your article appearing on the GeeksforGeeks main page and help other Geeks.
Please Improve this article if you find anything incorrect by clicking on the "Improve Article" button below.