DBSCAN Clustering

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DBSCAN (Density-Based Spatial Clustering of Applications with Noise)

Why we need DBSCAN?

While K-Means clustering is a popular choice, it struggles with noisey data as it considers outliers as a cluster. Enters DBSCAN, an algorithm that not only detects the outliers but also removes them.


So now, let's understand, How this algorithm works?

 




Key Ingredients of DBSCAN

  • Epsilon (ε): Think of this as the maximum distance between two points for them to be neighbors.
  • MinPts: The minimum number of points required to form a dense cluster.
  • Core Point: A point with at least MinPts neighbors within its ε-radius.
  • Border Point: Close to a core point but with fewer than MinPts neighbors.
  • Noise Point: Points that don’t fit into any cluster – the outliers.

Why Choose DBSCAN?

  • Outlier Detection: Naturally identifies noise points, making it great for spotting anomalies.
  • No Predefined Clusters: Unlike K-Means, you don’t need to specify the number of clusters beforehand.
  • Flexibility: Handles clusters of various shapes and sizes, perfect for complex datasets.

Tips for Tuning DBSCAN

Choosing the right values is key:

  • Epsilon (ε): A small eps might leave many points as noise, while a large eps could merge distinct clusters. Plot the k-distance graph to find the "elbow" point – a sweet spot for eps.
  • MinPts: Typically set to twice the number of dimensions. Adjust based on your data and domain knowledge.

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