DBSCAN

DBSCAN

DBSCAN

Description

  • DBSCAN stands for Density Based Spatial Clustering of Applications with Noise.
  • It is an unsupervised ML algorithm used for segregating high-density clusters from those having low density.

Why to use

To create data point clusters based on density.

When to use

When you want to convert data into clusters based on their density.

When not to use

For textual data

Prerequisites

  • The number of clusters need not be specified.
  • The selected variables need to be scaled before clustering.
  • The number of data points in a cluster should be greater than or equal to the dimension.
  • There should be at least two variables/features that can be selected as independent variables.

Input

Any numerical dataset containing unlabeled data

Output

  • Clustered Data with anomalies (noise points)
  • Cluster Plot
  • Silhouette Coefficient

Statistical Methods used

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Limitations

  • Does not work well with high dimensional data
  • Choosing an epsilon value can be difficult

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