lsdbc

0th

Percentile

Locally Scaled Density Based Clustering

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

Keywords
lsdbc
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.

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

Aliases
  • lsdbc
Examples
# NOT RUN {
x <- runif(20,-1,1)
y <- runif(20,-1,1)
dataset <- cbind(x,y)
l <- lsdbc(dataset, 7,3,"euclidean")
l
# }
Documentation reproduced from package lsdbc, version 0.1.0, License: GPL

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