Package 'fpc' - open source solution for an Internet free, intelligent ...

38 dbscan
dbscan
DBSCAN density reachability and connectivity clustering
Description
Generates a density based clustering of arbitrary shape as introduced in Ester et al. (1996).
Usage
dbscan(data, eps, MinPts = 5, scale = FALSE, method = c("hybrid", "raw",
"dist"), seeds = TRUE, showplot = FALSE, countmode = NULL)
## S3 method for class ’dbscan’
print(x, ...)
## S3 method for class ’dbscan’
plot(x, data, ...)
## S3 method for class ’dbscan’
predict(object, data, newdata = NULL,
predict.max=1000, ...)
Arguments
data
eps
MinPts
scale
method
seeds
showplot
countmode
x
object
newdata
predict.max
data matrix, data.frame, dissimilarity matrix or dist-object
Reachability Distance
Reachability minimum no. of points
scale the data
"dist" treats data as distance matrix (relatively fast but memory expensive),
"raw" treats data as raw data and avoids calculating a distance matrix (saves
memory but may be slow), "hybrid" expects also raw data, but calculates partial
distance matrices (very fast with moderate memory requirements )
FALSE to not include the isseed-vector in the dbscan-object
0 = no plot, 1 = plot per iteration, 2 = plot per subiteration
NULL or vector of point numbers at which to report progress
object of class dbscan.
object of class dbscan.
matrix or data.frame with raw data to predict
max. batch size for predictions
... Further arguments transferred to plot methods.