OPTICSclustering function

OPTICS Clustering

OPTICS Clustering

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

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

...

Returns

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

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.

Author(s)

Michael Thrun

See Also

optics

Examples

data('Hepta') out=OPTICSclustering(Hepta$Data,MaxRadius=NULL,RadiusThreshold=NULL,minPts=NULL,PlotIt = FALSE)
  • Maintainer: Michael Thrun
  • License: GPL-3
  • Last published: 2023-10-19