DBSCAN: DBSCAN

In FCPS: Fundamental Clustering Problems Suite

DBSCAN

Description

Density-Based Spatial Clustering of Applications with Noise of [Ester et al., 1996].

Usage

DBSCAN(Data,Radius,minPts,Rcpp=TRUE,

PlotIt=FALSE,UpperLimitRadius,...)

Arguments

Data	[1:n,1:d] matrix of dataset to be clustered. It consists of n cases of d-dimensional data points. Every case has d attributes, variables or features.
Radius	Eps [Ester et al., 1996, p. 227] neighborhood in the R-ball graph/unit disk graph), size of the epsilon neighborhood. If NULL, automatic estimation is performed using insights of [Ultsch, 2005].
minPts	Number of minimum points in the eps region (for core points). In principle minimum number of points in the unit disk, if the unit disk is within the cluster (core) [Ester et al., 1996, p. 228]. If NULL, 2.5 percent of points is selected.
Rcpp	If TRUE: fast Rcpp implementation of mlpack is used. FALSE uses dbscan package.
PlotIt	Default: FALSE, If TRUE plots the first three dimensions of the dataset with colored three-dimensional data points defined by the clustering stored in Cls
UpperLimitRadius	Limit for radius search, experimental
...	Further arguments to be set for the clustering algorithm, if not set, default arguments are used.

Details

To simplify, the radius works as follows. When DBSCAN looks at a data point, it draws a circle around it with a Radius. If there are enough data points inside that circle, they become a group. Choosing the right Radius depends on how spread out your data points are.

This is the minimum number of data points minPts that should be inside the circle for DBSCAN to consider them a group. If there are fewer data points than this number, the data point might be considered as noise or an outlier. The minPts value helps control how strict or lenient the algorithm is when forming groups.

Value

List of

Cls	[1:n] numerical vector defining the clustering; this classification is the main output of the algorithm. Points which cannot be assigned to a cluster will be reported as members of the noise cluster with 0.
Object	Object defined by clustering algorithm as the other output of this algorithm

Author(s)

Michael Thrun

References

[Ester et al., 1996] Ester, M., Kriegel, H.-P., Sander, J., & Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise, Proc. Kdd, Vol. 96, pp. 226-231, 1996.

[Ultsch, 2005] Ultsch, A.: Pareto density estimation: A density estimation for knowledge discovery, In Baier, D. & Werrnecke, K. D. (Eds.), Innovations in classification, data science, and information systems, (Vol. 27, pp. 91-100), Berlin, Germany, Springer, 2005.

Examples

data('Hepta')

out=DBSCAN(Hepta$Data,Radius=NULL,minPts=NULL,PlotIt=FALSE)

## Not run: 
#search for right parameter setting by grid search
data("WingNut")
Data = WingNut$Data
DBSGrid <- expand.grid(
  Radius = seq(from = 0.01, to = 0.3, by = 0.02),
  minPTs = seq(from = 1, to = 50, by = 2)
)
BestAcc = c()
for (i in seq_len(nrow(DBSGrid))) {
  parameters <- DBSGrid[i,]
  Cls9 = DBSCAN(
    Data,
    minPts = parameters$minPTs,
    
    Radius = parameters$Radius,
    PlotIt = F,
    
    UpperLimitRadius = parameters$Radius
  )$Cls
  if (length(unique(Cls9)) < 5)
    BestAcc[i] = ClusterAccuracy(WingNut$Cls,
                                    
                                    Cls9) * 100
  else
    BestAcc[i] = 50
}
max(BestAcc)
which.max(BestAcc)
parameters <- DBSGrid[13,]

Cls9 = DBSCAN(
  Data,
  minPts = parameters$minPTs,
  Radius = parameters$Radius,
  UpperLimitRadius = parameters$Radius, 
  PlotIt = TRUE
)$Cls

## End(Not run)

FCPS documentation built on Nov. 5, 2025, 7:44 p.m.