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DBSCAN—Wolfram Language Documentation

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• See Also
  • FindClusters
  • ClusterClassify
  • ClusteringComponents
  • ClusteringTree
  • Dendrogram
  • DimensionReduction
  • Methods
  • Agglomerate
  • GaussianMixture
  • JarvisPatrick
  • KMeans
  • KMedoids
  • MeanShift
  • NeighborhoodContraction
  • SpanningTree
  • Spectral
• Tech Notes
  • Partitioning Data into Clusters

• Method for FindClusters, ClusterClassify and ClusteringComponents.
• Partitions data into clusters of similar elements using density-based spatial clustering of applications with noise (DBSCAN).

Find clusters of nearby values using the "DBSCAN" method:

Train the ClassifierFunction on a list of colors using the "DBSCAN" method:

Gather the elements by their class number:

Create random 2D vectors:

Plot clusters in data found using the "DBSCAN" method:

Obtain a random list of times:

Train the ClassifierFunction using the "DBSCAN" method:

Obtain the cluster assignment and cluster the data:

Train the ClassifierFunction using the "DBSCAN" method:

Noise points are labeled as Missing["Anomalous"]:

Cluster string data using edit distance:

Cluster data using Manhattan distance:

Find clusters by specifying the "NeighborhoodRadius" suboption:

Define a set of two-dimensional data points, characterized by four somewhat nebulous clusters:

Plot clusters in data found using the "DBSCAN" method:

Plot different clusterings of data using the "DBSCAN" method by varying the "NeighborhoodRadius":

Find clusters by specifying the "NeighborsNumber" suboption:

Create random 2D vectors:

Plot clusters in data found using the "DBSCAN" method:

Plot different clusterings of data using the "DBSCAN" method by varying the "NeighborsNumber":

Define a set of two-dimensional data points, characterized by four somewhat nebulous clusters:

Plot clusters in data using the "DBSCAN" method:

Plot different clusterings of data using the "DBSCAN" method by varying the "NeighborsNumber":

Train the ClassifierFunction, which labels outliers as Missing["Anomalous"]:

Use the trained ClassifierFunction to identify the outliers:

Train the ClassifierFunction by dropping outliers and finding new cluster assignments:

Similarly, find clusters of nearby values with outliers:

Remove outliers using the "DropAnomalousValues" suboption:

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