Experimental implementation of the OPTICS algorithm.
lv_optics(edges, neighbors, eps = Inf, minPts = nrow(neighbors), eps_cl, xi,
useQueue = TRUE, verbose = getOption("verbose", TRUE))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.
An adjacency matrix of the type produced by randomProjectionTreeSearch
See optics.
See optics.
See optics.
See optics.
Whether to process points in order of core distance. (See note.)
Vebosity level.
An optics object.
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.
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.