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bigMap (version 2.3.1)

Big Data Mapping

Description

Unsupervised clustering protocol for large scale structured data, based on a low dimensional representation of the data. Dimensionality reduction is performed using a parallelized implementation of the t-Stochastic Neighboring Embedding algorithm (Garriga J. and Bartumeus F. (2018), ).

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Version

Install

install.packages('bigMap')

Monthly Downloads

80

Version

2.3.1

License

GPL-3

Maintainer

Joan Garriga

Last Published

June 30th, 2020

Functions in bigMap (2.3.1)

bdm.merge.s2nr

Merging of clusters based on signal-to-noise-ratio.
bdm.dMap

Class density maps
bdm.cost

ptSNE cost & size plot.
bdm.example

Example dataset
bdm.boxp

Clustering statistics box-plot.
bdm.local

Set/get default local machine name or IP address
bdm.fName

Default bdm file name
bdm.init

Create bdm instance
bdm.dMap.plot

Class density maps plot.
bdm.labels

Get data-point clustering labels.
bdm.optk.s2nr

Find optimal number of clusters based on signal-to-noise-ratio.
bdm.pakde.plot

Plot paKDE (density landscape)
bdm.ptsne

Parallelized t-SNE
bdm.pakde

Perplexity-adaptive kernel density estimation
bdm.wtt.plot

Plot WTT (clustering)
bdm.wtt

Watertrack transform (WTT)
bdm.ptsne.plot

Plot ptSNE (low-dimensional embedding)
bdm.qMap

ptSNE quantile-maps
bdm.save

Save bdm instance
bdm.optk.plot

Plots the signal-to-nois-ratio as a function of the number of clusters.
bdm.scp

Transfer bdm instance to a remote machine.
bdm.mybdm

Set/get default path for mybdm