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Memory efficient clustering in R for large datasets
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README.md

Rclusterpp -- Large-scale hierarchical clustering in R

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Rclusterpp provides flexible native hierarchical clustering routings optimized for performance and minimal memory requirements. In particular Rclusterpp includes "stored data" clustering implementations with O(n) memory footprints. Rclusterpp has been successfully used to cluster 100,000s of observations.

Rclusterpp makes extensive use of Rcpp for integration with R, and the Eigen matrix library (via RcppEigen). Rclusterpp provides a R interface to its internal libraries that can be used in place of stats::hclust and provides linkable libraries for use by downstream packages.

Explore the unit tests inst/unit_tests and examples directory inst/examples for examples on how to use Rclusterpp directly within R, or as a linkable library for use with other native code. Note that some of the examples require the inline package.

Rclusterpp uses OpenMP internally for concurrent execution. By default, as many threads as processors are created. To control the number of threads set the OMP_NUM_THREADS environment variable.

Installation

Rclusterpp installation instructions can be found on the project wiki.

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