lsdbc: Locally Scaled Density Based Clustering

In lsdbc: Locally Scaled Density Based Clustering

Description

Generate a locally scaled density based clustering as proposed by Bicici and Yuret (2007).

Usage

lsdbc(data, k, alpha, jarak = c("euclidean", "manhattan", "canberra", "geodesic"))

Arguments

data	Dataset consists of two variables (x,y) indicating coordinates of each data (point)
k	Number of neighbor to be considered
alpha	Parameter for determining local maximum
jarak	Type of distance to be used, the options are c("euclidean", "manhattan", "canberra", "geodesic")

Value

This function returns a list with the following objects:

data	a dataframe of the dataset used.
cluster	an integer vector coding cluster membership, 0 indicates a noise and cluster start at 1.
parameter	consist of parameter k and alpha.

Author(s)

Fella Ulandari and Robert Kurniawan

References

Bicici, E., & Yuret, D. (2007). Locally Scaled Density Based Clustering. International Conference on Adaptive and Natural Computing Algorithms (pp. 739-748). Berlin: Springer.

See Also

https://doi.org/10.1007/978-3-540-71618-1_82

Examples

x <- runif(20,-1,1)
y <- runif(20,-1,1)
dataset <- cbind(x,y)
l <- lsdbc(dataset, 7,3,"euclidean")
l

lsdbc documentation built on July 1, 2020, 6:01 p.m.