daltoolbox (version 1.2.747)

cluster_dbscan: DBSCAN

Description

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

Usage

cluster_dbscan(minPts = 3, eps = NULL)

Value

returns a dbscan object

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.

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