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Overview

Description

Centroid-based clustering and density-based clustering are two popular approaches used for grouping data into clusters:

Centroid-Based Clustering:

  • Relies on defining clusters around central points (centroids).
  • Assigns data points to the nearest centroid based on distance (e.g., Euclidean distance).
  • Common algorithm: k-means.
  • Works well with spherical, evenly sized clusters.
  • Sensitive to initialization (choice of initial centroids) and outliers.
  • Requires specifying the number of clusters (k) in advance.

Density-Based Clustering:

  • Groups data based on areas of high density separated by areas of low density.
  • Identifies clusters of arbitrary shapes and sizes.
  • Can detect noise and outliers as points not belonging to any cluster.
  • Common algorithm: DBSCAN (Density-Based Spatial Clustering of Applications with Noise).
  • Does not require specifying the number of clusters but requires density parameters like eps (distance threshold) and minPts (minimum points per cluster).

Key Difference:

  • Centroid-based clustering depends on distance to a central point, while density-based clustering focuses on the density of data points.
  • Density-based methods are better for handling complex shapes and outliers, whereas centroid-based methods are computationally simpler and suited for compact clusters.