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Mindmap

DBSCAN Clustering Algorithm

ROSA KAROL GARCIA PALOS . Spanisch

DBSCAN Clustering Algorithm

Inhalt der Mindmap

DBSCAN Clustering Algorithm

  1. Definition

    • Essencial technique
    • Used in domains and industries
    • Acrostic
      • D
        • Density
      • B
        • Based
      • S
        • Spatial
      • C
        • Clustering
      • A
        • Applications
      • N
        • Noise

  2. Why we need it?

    • To create arbitrary shapes
      • Varying densities
    • Detects noise

  3. Reachability and Connectivity

    • Reachability
      • A data point can be accessed from another data point directly or indirectly
    • Connectivity
      • If two data points belong to the same cluster or not.
    • Two points can be
      • Directly Density-Reachable
      • Density-Reachable
      • Density-Connected

  4. Parameter Selection

    • MinPoints
      • Dimensions + 1
    • Epsilon
      • Can be decided from the K-distance graph.

  5. Proposed by

    • Martin Ester (1996)

  6. Characteristics

    • It's robust to outliers.
    • Parameters
      • Epsilon
        • Radius
      • MinPoints
        • Central point

  7. Carlos Daniel Cedeño Ruiz

Bilder und Dateien der Mindmap

  • DBSCAN Clustering Algorithm
  • DBSCAN Clustering Algorithm
  • DBSCAN Clustering Algorithm
  • DBSCAN Clustering Algorithm
  • DBSCAN Clustering Algorithm
  • DBSCAN Clustering Algorithm
  • DBSCAN Clustering Algorithm
  • DBSCAN Clustering Algorithm
  • DBSCAN Clustering Algorithm
  • DBSCAN Clustering Algorithm
  • DBSCAN Clustering Algorithm
  • DBSCAN Clustering Algorithm
  • DBSCAN Clustering Algorithm

Informationen

Beschreibung
Mindmap am DBSCAN Clustering Algorithm, erstellt von ROSA KAROL GARCIA PALOS . am 19/09/2021.
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