sscs: Search for Structure in Collective Systems

An implementation of the clustering algorithm introduced in Twomey et al. (2018), provided as an R package. Currently only implements a hard-clustering variant of this algorithm; the plan is to include an improved implementation of the soft-clustering variant in the future. The hard-clustering variant is the most practical for performance reasons, so it was made the focus of this initial release.

Compiling

This R package requires Rcpp and RcppEigen (available from CRAN). To compile from the commandline, cd to the parent directory of the sscs repository and run

$ R CMD build sscs
$ R CMD install sscs_0.1.tar.gz

Usage

The package includes data for a simple example using US state annual average temperature and precipitation time series data, publicly available from NOAA. While simple, the results are intuitive and easy to visualize.

Univariate example using temperature alone:

# create a new 'sscs' S3 clustering object
sscs <- new_sscs(US_state_temperature)

# run clustering
sscs <- run(sscs, nclusters=12, ncores=16, nreps=400)

# get the cluster assignments
cl <- assignments(sscs)

# convenience function using the 'maps' library for showing the result
plot_us_states_example(cl)

Multivariate example combining temperature and precipitation:

# combine measurements
X <- cbind(US_state_temperature, US_state_precipitation)

# js describes how columns of X are grouped into multi-variate variables
nstates <- ncol(US_state_temperature)
js      <- rep(1:nstates, 2)

# run clustering on states
sscs <- new_sscs(X, js)
sscs <- run(sscs, nclusters=12, ncores=16, nreps=400)
cl   <- assignments(sscs)

# show the result
plot_us_states_example(cl)

Credit

Please cite Twomey et al. (2018) and include a link to this repository if you use this code in an academic publication.

Testing, bug reports, and code contributions very welcome.

Copyright (c) 2018, 2019 Colin Twomey. Shared under a GNU GPLv3 license (see COPYING).