Learn R Programming

largeVis (version 0.2.1.1)

High-Quality Visualizations of Large, High-Dimensional Datasets

Description

Implements the largeVis algorithm (see Tang, et al. (2016) ) for visualizing very large high-dimensional datasets. Also very fast search for approximate nearest neighbors; outlier detection; and optimized implementations of the HDBSCAN*, DBSCAN and OPTICS clustering algorithms; plotting functions for visualizing the above.

Copy Link

Version

Install

install.packages('largeVis')

Monthly Downloads

77

Version

0.2.1.1

License

GPL-3

Issues

Pull Requests

Stars

Forks

Repository

https://github.com/elbamos/largeVis

Maintainer

Last Published

February 16th, 2018

Functions in largeVis (0.2.1.1)

Apply the LargeVis algorithm for visualizing large high-dimensional datasets.

Local Outlier Factor Score

as.dendrogram.hdbscan

as.dendrogram.hdbscan

buildEdgeMatrix

Build an nearest-neighbor graph weighted by distance.

neighborsToVectors

A utility function to convert a k-NN graph to a pair of 0-indexed vectors of indices.

Project a distance matrix into a lower-dimensional space.

Calculate pairwise Euclidean or angular distances efficiently

Visualize an embedding by plotting with images

manifoldMapStretch

manifoldMapStretch

largeVis-package

largeVis: high-quality visualizations for large, high-dimensionality datasets

Visualize an embedding by ggplotting with images

randomProjectionTreeSearch

Find approximate k-Nearest Neighbors using random projection tree search.