This is an implementation of the largeVis
algorithm described in (https://arxiv.org/abs/1602.00370). It also incorporates:
Rcpp
and OpenMP
. See the Benchmarks file for performance details.HDBSCAN
OPTICS
DBSCAN
library(clusteringdatasets) library(largeVis, quietly = TRUE) library(ggplot2) data(Aggregation) vis <- largeVis(t(as.matrix(Aggregation[, 1:2])), sgd_batches = 1) clusters <- hdbscan(vis, K = 2) theme_set( theme_bw() %+replace% theme( legend.title = element_text(size = rel(0.8), face = "bold"), legend.margin = unit(0, "cm"), legend.position = "right", legend.key.size = unit(2, "lines"), legend.text = element_text(size = unit(8, "points")), axis.title.y = element_text(angle = 90), axis.text = element_text(size = rel(0.7)), plot.margin = unit(c(0, 0.5, 1, 0), "lines"), axis.title = element_text(size = rel(0.8), face = "bold"), title = element_text(size = rel(0.9)) ) ) show(gplot(clusters, Aggregation[, 1:2]) + scale_x_continuous("", labels = NULL) + scale_y_continuous("", labels = NULL))
clang 4.0
by default. Since R 3.4 is new, I'm not able to provide advice, but am interested in hearing of any issues and any workarounds to issues that you may discover. Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.