This package implements fuzzy DBScan with fuzzy core and fuzzy border.
Therefore, it provides a method to initialize and run the algorithm and
a function to predict new data w.t.h. of R6. The package is build upon
the paper “Fuzzy Extensions of the DBScan algorithm” from Dino Ienco and
Gloria Bordogna. The predict function assigns new data based on the same
criteria as the algorithm itself. However, the prediction function
freezes the algorithm to preserve the trained cluster structure and
treats each new prediction object individually.
You can install the development version of FuzzyDBScan from GitHub with:
# install.packages("devtools")
devtools::install_github("henrifnk/FuzzyDBScan")The following example shows how Fuzzy DBScan works with the
multishapes data set from the factoextra package. We set the range
of
library(factoextra)
dta = multishapes[, 1:2]
eps = c(0, 0.2)
pts = c(3, 15)Next, we train the DBScan based on dta, eps and pts. This is done
by initializing the R6 object. FuzzyDBScan contains a scatterplot
method, where the clusters (colours) and fuzzieness (transparency) are
plotted for any two features.
library(FuzzyDBScan)
cl = FuzzyDBScan$new(dta, eps, pts)
cl$plot("x", "y")
FuzzyDBScan is equipped with a prediction method. This method freezes
the algorithm such that new data points are not used for updating the
cluster structure itself.<s Each new data point is then assigned a
cluster and fuzziness individually by the same rules as during training.
x <- seq(min(dta$x), max(dta$x), length.out = 50)
y <- seq(min(dta$y), max(dta$y), length.out = 50)
p_dta = expand.grid(x = x, y = y)
p = cl$predict(p_dta, FALSE)
ggplot(p, aes(x = p_dta[, 1], y = p_dta[, 2], colour = as.factor(cluster))) +
geom_point(alpha = p$dense)
- Ienco, Dino, and Gloria Bordogna. Fuzzy extensions of the DBScan clustering algorithm. Soft Computing 22.5 (2018): 1719-1730.