Density Based Clustering

density based Clustering

Density based clustering clusters on the basis of density of the data points in the feature space. Unlike partitional clustering the number of clusters need not be specified.

Some of the commonly used methods in partitional clustering include:

DBSCAN ( Density-Based Spatial Clustering of Applications with Noise)
HDBSCAN (Hierarchical Density-Based Spatial Clustering of Applications with Noise)
OPTICS ( Ordering Points To Identify the Clustering Structure)

Pros:

Robust to Noise
No assumptions of cluster shapes

Cons:

Not suitable for high dimensional data
Sensitive to choice of parameters

Example of Density Based Clustering

Overview

Partitional Clustering

Hierarchical Clustering

Distance Measures

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