DBSCAN Clustering

 There are different approaches and algorithms to perform clustering tasks which can be divided into three sub-categories:

• Partition-based clustering: E.g. k-means, k-median

• Hierarchical clustering: E.g. Agglomerative, Divisive

• Density-based clustering: E.g. DBSCAN

Density Based Clustering

Partition-based and hierarchical clustering techniques are highly efficient with normal shaped clusters. However, when it comes to arbitrary shaped clusters or detecting outliers, density-based techniques are more efficient.

For example, the dataset in the figure below can easily be divided into three clusters using k-means algoritm.

Consider the following figures:

The data points in these figures are grouped in arbitrary shapes or include outliers. Density-based clustering algorithms are very efficient at finding high-density regions and outliers. It is very important to detect outliers for some tasks, e.g. anomaly detection.