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 density-based 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.|
|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|
Eps : Radius of circle
minPts : It is the minimum no. of points that must exist in the vicinity of eps.
- DBSCAN Clustering in ML | Density based clustering
- ML | Hierarchical clustering (Agglomerative and Divisive clustering)
- ML | Fuzzy Clustering
- ML | Spectral Clustering
- ML | Mean-Shift Clustering
- ML | K-Medoids clustering with example
- ML | Classification vs Clustering
- K means Clustering - Introduction
- Criterion Function Of Clustering
- ML | Types of Linkages in Clustering
- Different Types of Clustering Algorithm
- Clustering in Machine Learning
- ML | OPTICS Clustering Explanation
- ML | Determine the optimal value of K in K-Means Clustering
- Image compression using K-means 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 firstname.lastname@example.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.