cluster_dbscan: DBSCAN

In daltoolbox: Leveraging Experiment Lines to Data Analytics

DBSCAN

Description

Density-Based Spatial Clustering of Applications with Noise using dbscan::dbscan.

Usage

cluster_dbscan(minPts = 3, eps = NULL)

Arguments

minPts	minimum number of points
eps	distance value

Details

Discovers clusters as dense regions separated by sparse areas. Hyperparameters are eps (neighborhood radius) and minPts (minimum points). If eps is missing, it is estimated from the kNN distance curve elbow.

Value

returns a dbscan object

References

Ester, M., Kriegel, H.-P., Sander, J., Xu, X. (1996). A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise.

Examples

# setup clustering
model <- cluster_dbscan(minPts = 3)

#load dataset
data(iris)

# build model
model <- fit(model, iris[,1:4])
clu <- cluster(model, iris[,1:4])
table(clu)

# evaluate model using external metric
eval <- evaluate(model, clu, iris$Species)
eval

daltoolbox documentation built on Nov. 5, 2025, 7:09 p.m.