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CMakeLists.txt (714, 2018-02-27)
LICENSE (1066, 2018-02-27)
source/ (0, 2018-02-27)
source/cluster.cu (11549, 2018-02-27)
source/cluster.h (920, 2018-02-27)
source/main.cpp (3260, 2018-02-27)
source/utility.cpp (2087, 2018-02-27)
source/utility.h (986, 2018-02-27)

Efficient hierarchical clustering for single-dimensional data using CUDA ======================================================================== This repository contains the implementation of the clustering algorithm presented in the paper: - [Adam Rehn, Aidan Possemiers, and Jason Holdsworth. Efficient hierarchical clustering for single-dimensional data using CUDA. In *Proceedings of the Australasian Computer Science Week Multiconference*, ACSW '18, 14:1–14:10. New York, NY, USA, 2018. ACM.](http://doi.acm.org/10.1145/3167918.3167929) Building from source -------------------- The following dependencies are required in order to build Cluster1D from source: - A modern, C++11-compliant compiler (Clang, GCC, Visual Studio 2015 or newer) - [CMake](https://cmake.org/) 3.5 or newer - [NVIDIA CUDA Toolkit](https://developer.nvidia.com/cuda-toolkit) To build under macOS or Linux, run the following commands: ``` mkdir build && cd build cmake -DCMAKE_BUILD_TYPE=Release .. cmake --build . ``` To build under Windows, run the following commands: ``` mkdir build && cd build cmake -A x64 .. cmake --build . --config Release ``` Performing clustering --------------------- **(Running the built Cluster1D executable without any arguments will also print these usage instructions.)** Usage: `Cluster1D [--r-format]` Input file should be a text file containing floats, one per line. Supported linkage metrics: - `single` - `complete` Specifying `--r-format` will transform the output into a form compatible with the default dendrogram representation used by the R `hclust()` function. Output is written to stdout in CSV format.

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