lv_optics: lv_optics
In largeVis: High-Quality Visualizations of Large, High-Dimensional Datasets

Description Usage Arguments Details Value Note References

Experimental implementation of the OPTICS algorithm.

1 2	lv_optics(edges, neighbors, eps = Inf, minPts = nrow(neighbors), eps_cl, xi, useQueue = TRUE, verbose = getOption("verbose", TRUE))

`edges`	A weighted graph of the type produced by `buildEdgeMatrix`. Alternatively, a `largeVis` object, in which case `edges` and `neighbors` will be taken from the `edges` and `knns` parameters, respectively.
`neighbors`	An adjacency matrix of the type produced by `randomProjectionTreeSearch`
`eps`	See `optics`.
`minPts`	See `optics`.
`eps_cl`	See `optics`.
`xi`	See `optics`.
`useQueue`	Whether to process points in order of core distance. (See note.)
`verbose`	Vebosity level.

This is an implementation of the OPTICS algorithm that attempts to leverage the largeVis nearest-neighbor search.

This implementation does not use the OPTICS neighbor-search strategy, in favor of using the pre-calculated neighbor matrix produced incidentally by largeVis. It is therefore a variant of OPTICS rather than an implementation of the original, and the results vary slightly from those obtained by the implementations in ELKI and the dbscan package.

An optics object.

The useQueue parameter controls the order in which points that have not yet been visisted are processed. If FALSE, points are processed in order of rows. If TRUE, they are processed in ascending order of core distance. FALSE is more compatible with the implementations in the dbscan package and in the ELKI Java clustering package. TRUE may produce preferrable results.

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

largeVis documentation built on Feb. 17, 2018, 1:01 a.m.