Skip to main content
torch.js has not been released yet.
torch.js logotorch.js logotorch.js
PlaygroundContact
Login
Documentation
IntroductionType SafetyTensor ExpressionsTensor IndexingEinsumEinopsAutogradTraining a ModelProfiling & MemoryPyTorch MigrationBest PracticesRuntimesPerformancePyTorch CompatibilityBenchmarksDType Coverage
IntroductionRenderer GalleryRenderersAnalysis
computeQuantilescomputePercentilescomputeCorrelationcomputeCovariancedetectOutliersdetectOutliersZScorecomputeExtendedStatsdescribeExtendedStatsQuantileResultCorrelationResultOutlierResultExtendedStatscreateDBSCANdbscanDBSCANDBSCAN.fitDBSCAN.destroyDBSCANOptionsDBSCANResultcreateHierarchicalClusteringhierarchicalClusteringHierarchicalClusteringHierarchicalClustering.fitHierarchicalClustering.destroyLinkageMethodDistanceMetricHierarchicalOptionsDendrogramNodeHierarchicalResultcomputeHistogramgetHistogramCentersnormalizeHistogramHistogramResultcreateKDEkdeKDEKDE.fitKDE.destroyKernelTypeKDEOptionsKDEResultcreateKMeanskmeansKMeansKMeans.fitKMeans.destroyKMeansOptionsKMeansResultcreatePCApcaPCAPCA.fitPCA.destroyPCAOptionsPCAResultthrottleProgressconsoleProgressProgressInfoProgressCallbackcomputeStatscomputeMinMaxdescribeStatsTensorStatscreateTSNEtsneTSNETSNE.fitTSNE.destroyTSNEOptionsTSNEResultcreateUMAPumapUMAPUMAP.fitUMAP.destroyUMAPOptionsUMAPResult
torch.js· 2026
LegalTerms of UsePrivacy Policy
/
/
  1. docs
  2. viz
  3. viz
  4. analysis
  5. DBSCANResult

viz.analysis.DBSCANResult

export interface DBSCANResult {
  /** Cluster labels (-1 for noise) */
  labels: Int32Array;
  /** Number of clusters found (excluding noise) */
  numClusters: number;
  /** Indices of core points */
  corePointIndices: number[];
  /** Indices of noise points */
  noisePointIndices: number[];
  /** Number of points in each cluster */
  clusterSizes: number[];
  /** Epsilon used */
  eps: number;
  /** Min points used */
  minPoints: number;
  /** Number of points */
  numPoints: number;
}
labels(Int32Array)
– Cluster labels (-1 for noise)
numClusters(number)
– Number of clusters found (excluding noise)
corePointIndices(number[])
– Indices of core points
noisePointIndices(number[])
– Indices of noise points
clusterSizes(number[])
– Number of points in each cluster
eps(number)
– Epsilon used
minPoints(number)
– Min points used
numPoints(number)
– Number of points

Result of DBSCAN clustering.

Previous
DBSCANOptions
Next
DendrogramNode