OPTICSclustering: OPTICS Clustering

In FCPS: Fundamental Clustering Problems Suite

OPTICS Clustering

Description

OPTICS (Ordering points to identify the clustering structure) clustering algorithm [Ankerst et al.,1999].

Usage

OPTICSclustering(Data, MaxRadius,RadiusThreshold, minPts = 5, PlotIt=FALSE,...)

Arguments

Data	[1:n,1:d] matrix of dataset to be clustered. It consists of n cases of d-dimensional data points. Every case has d attributes, variables or features.
MaxRadius	Upper limit neighborhood in the R-ball graph/unit disk graph), size of the epsilon neighborhood (eps) [Ester et al., 1996, p. 227]. If NULL, automatic estimation is done using insights of [Ultsch, 2005].
RadiusThreshold	Threshold to identify clusters (RadiusThreshold <= MaxRadius), if NULL 0.9*MaxRadius is set.
minPts	Number of minimum points in the eps region (for core points). In principle minimum number of points in the unit disk, if the unit disk is within the cluster (core) [Ester et al., 1996, p. 228]. If NULL, its 2.5 percent of points.
PlotIt	Default: FALSE, If TRUE plots the first three dimensions of the dataset with colored three-dimensional data points defined by the clustering stored in Cls
...	Further arguments to be set for the clustering algorithm, if not set, default arguments are used.

Details

...

Value

List of

Cls	[1:n] numerical vector defining the clustering; this classification is the main output of the algorithm. Points which cannot be assigned to a cluster will be reported as members of the noise cluster with 0.
Object	Object defined by clustering algorithm as the other output of this algorithm

Author(s)

Michael Thrun

References

[Ankerst et al.,1999] Mihael Ankerst, Markus M. Breunig, Hans-Peter Kriegel, Joerg Sander: OPTICS: Ordering Points To Identify the Clustering Structure, ACM SIGMOD international conference on Management of data, ACM Press, pp. 49-60, 1999.

[Ester et al., 1996] Ester, M., Kriegel, H.-P., Sander, J., & Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise, Proc. Kdd, Vol. 96, pp. 226-231, 1996.

[Ultsch, 2005] Ultsch, A.: Pareto density estimation: A density estimation for knowledge discovery, In Baier, D. & Werrnecke, K. D. (Eds.), Innovations in classification, data science, and information systems, (Vol. 27, pp. 91-100), Berlin, Germany, Springer, 2005.

See Also

optics

Examples

data('Hepta')
out=OPTICSclustering(Hepta$Data,MaxRadius=NULL,RadiusThreshold=NULL,minPts=NULL,PlotIt = FALSE)

FCPS documentation built on Oct. 19, 2023, 5:06 p.m.