lv_dbscan: lv_dbscan

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

Description

Implementation of the DBSCAN algorithm using largeVis datastructures.

Usage

lv_dbscan(edges, neighbors, eps = Inf, minPts = nrow(neighbors - 1),
  verbose = getOption("verbose", TRUE))

Arguments

edges	An 'edgematrix' object. 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 dbscan.
minPts	See dbscan.
verbose	Vebosity level.

Details

The DBSCAN algorithm attempts to find clusters of a minimum density given by eps. This implementation leverages the nearest neighbor data assembled by largeVis.

Value

A dbscan object.

References

Martin Ester, Hans-Peter Kriegel, Jorg Sander, Xiaowei Xu (1996). Evangelos Simoudis, Jiawei Han, Usama M. Fayyad, eds. A density-based algorithm for discovering clusters in large spatial databases with noise. Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96). AAAI Press. pp. 226-231. ISBN 1-57735-004-9.

elbamos/largeVis documentation built on May 16, 2019, 2:58 a.m.