Explain DBSCAN Clustering technique and in what terms DBSCAN is better than K- Means Clustering?

Data Science, AI and ML
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DBSCAN( Density Based) clustering technique is an unsupervised approach which splits the vectors into different groups based on the minimum distance and number of points lying in that range. In DBSCAN Clustering we have two significant parameters –

Epsilon – The minimum radius or distance between the two data points to tag them in the same cluster.

Min – Sample Points – The number of minimum sample which should fall under that range to be identified as one cluster.

DBSCAN Clustering technique has few advantages over other clustering algorithms –

  1. In DBSCAN we do not need to provide the fixed number of clusters. There can be as many clusters formed on the basis of the data points distribution. While in k nearest neighbour we need to provide the number of clusters we need to split our data into.

  2. In DBSCAN we also get a noise cluster identified which helps us in identifying the outliers. This sometimes also acts as a significant term to tune the hyper parameters of a model accordingly.