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