Difference between clustering algorithms

Advantage and Disadvantage of Hierarchical algorithm:

Advantages	Disadvantage
Does not require a number of clusters to be specified	Can never undo the previous step throughout the algorithm
Easy to implement	Generally has long runtime
Produce a Dendrogram, which helps with understanding the data	Sometimes difficult to identify the number of clusters by dendrogram

Difference between Hierarchical and K-mean Clustering:

K-mean	Hierarchical
Much more efficient	Can be slow for large data
Requires the number of clusters to be specified	Does not require a number of clusters to run
Give only one partitioning of data based on a predefined number of clusters	Give more than one partitioning depending on the resolution
Potentially returns different clusters each time, it is run due to random initialization of centroids	Always generate the same clusters

Advantage of DBSCAN:

1. Arbitrary shaped clusters

2. Robust to outliers

3. It does not require a specification of no. clusters

Difference between K-mean and Density-based: K-mean assigns all points to a cluster even if they don't belong in any cluster. Density-based clusters locate regions of high density and separate outliers without getting affected by noise.