HDBSCAN algorithm implementation in golang.
Written to run concurrently on CPU (uses all CPU cores by default).
Code originates from hdbscan go package which seems to be no longer publicly available (only way to access it is to run go get -u github.com/humilityai/hdbscan).
This package might be extended with other clustering algorithms in the future.
go get -u github.com/bringyour/cluster
import(
"github.com/bringyour/cluster/hdbscan"
)
func main() {
data := [][]float64{
[]float64{1,2,3},
[]float64{3,2,1},
}
minimumClusterSize := len(data)
minimumSpanningTree := true
// create
clustering, err := hdbscan.NewClustering(data, minimumClusterSize)
if err != nil {
panic(err)
}
// options
clustering = clustering.Verbose().OutlierDetection()
//run
clustering.Run(hdbscan.EuclideanDistance, hdbscan.VarianceScore, minimumSpanningTree)
// If using sampling, then can use the Assign() method afterwards on the total dataset.
}Verbose()will log the progress of the clustering to stdout.Voronoi()will add all points not placed in a cluster in the final clustering to th 5A21 eir nearest cluster. All unassigned data points outliers will be added to their nearest cluster.OutlierDetection()will mark all unassigned data points as outliers of their nearest cluster and provide aNormalizedDistancevalue for each outlier that can be interpreted as the probability that the data point is an outlier of that cluster.NearestNeighbor()specifies if an unassigned points "nearness" to a cluster should be based on it's nearest assigned neighboring data point in that cluster (default "nearness" is based on distance to centroid of cluster).Subsample(n int)specifies to only use the firstndata points in the clustering process. This speeds up the clustering. The remaining data points can be added to clusters using theAssign(data [][]float64)method after a successful clustering.OutlierClustering()will create a new cluster for the outliers of an existing cluster if the number of outliers is equal to or greater than the specified minimum-cluster-size.