This repository contains a header-only C++ library for k-means clustering. Initialization can be performed with user-supplied centers, random selection of points, weighted sampling with kmeans++ (Arthur and Vassilvitskii, 2007) or variance partitioning (Su and Dy, 2007). Refinement can be performed using the Hartigan-Wong method, Lloyd's algorithm or a custom mini-batch implementation. The Hartigan-Wong implementation is derived from the Fortran code in the R stats package, heavily refactored for more idiomatic C++.
kmeans is a header-only library, so it can be easily used by just #includeing the relevant source files and running compute():
#include "kmeans/Kmeans.hpp"
int ndim = 5;
int nobs = 1000;
std::vector<double> matrix(ndim * nobs); // column-major ndim x nobs matrix of coordinates
// Wrap your matrix in a SimpleMatrix.
kmeans::SimpleMatrix<
int, /* type of the column index */
double /* type of the data */
> kmat(ndim, nobs, matrix.data());
auto res = kmeans::compute(
kmat,
// initialize with kmeans++
kmeans::InitializeKmeanspp<
/* column index type */ int,
/* input matrix data type */ double,
/* cluster ID type */ int,
/* centroid type */ double
>(),
// refine with Lloyd's algorithm
kmeans::RefineLloyd<
/* column index type */ int,
/* input matrix data type */ double,
/* cluster ID type */ int,
/* centroid type */ double
>(),
ncenters
);
res.centers; // Matrix of centroid coordinates, stored in column-major format
res.clusters; // Vector of cluster assignments
res.details; // Details from the clustering algorithmSee the reference documentation for more details.
We can tune the clustering by passing options into the constructors of the relevant classes:
kmeans::InitializeVariancePartitionOptions vp_opt;
vp_opt.optimize_partition = false;
kmeans::InitializeVariancePartition<int, double, int, double> vp(vp_opt);
kmeans::RefineLloydOptions ll_opt;
ll_opt.max_iterations = 10;
ll_opt.num_threads = 3;
kmeans::RefineLloyd<int, double, int, double> ll(ll_opt);
auto res2 = kmeans::compute(kmat, pp, ll, ncenters);The initialization and refinement classes can themselves be swapped at run-time via pointers to their respective interfaces. This design also allows the kmeans library to be easily extended to additional methods from third-party developers.
std::unique_ptr<kmeans::Initialize<int, double, int, double> > init_ptr;
if (init_method == "random") {
init_ptr.reset(new kmeans::InitializeRandom<int, double, int, double>);
} else if (init_method == "kmeans++") {
kmeans::InitializeKmeansppOptions opt;
opt.seed = 42;
init_ptr.reset(new kmeans::InitializeKmeanspp<int, double, int, double>(opt));
} else {
// do something else
}
std::unique_ptr<kmeans::Refine<int, double, int, double> > ref_ptr;
if (ref_method == "random") {
kmeans::RefineLloydOptions opt;
opt.max_iterations = 10;
ref_ptr.reset(new kmeans::RefineLloyd<int, double, int, double>(opt));
} else {
kmeans::RefineHartiganWongOptions opt;
opt.max_iterations = 100;
opt.max_quick_transfer_iterations = 1000;
ref_ptr.reset(new kmeans::RefineHartiganWong<int, double, int, double>(opt));
}
auto res3 = kmeans::compute(kmat, *init_ptr, *ref_ptr, ncenters);Template parameters can also be altered to control the input and output data types.
As shown above, these should be set consistently for all classes used in compute().
While int and double are suitable for most cases, advanced users may wish to use other types.
For example, we might consider the following parametrization for various reasons:
kmeans::InitializeKmeanspp<
/* If our input data has too many observations to fit into an 'int', we
* might need to use a 'size_t' instead.
*/
size_t,
/* Perhaps our input data is in single-precision floating point to save
* space and to speed up processing.
*/
float,
/* If we know that we will never ask for more than 255 clusters, we can use
* a smaller integer for the cluster IDs to save space.
*/
uint8_t,
/* We still want our centroids and distances to be computed in high
* precision, even though the input data is only single precision.
*/
double
> initpp();If we want the within-cluster sum of squares, this can be easily calculated from the output of compute():
std::vector<double> wcss(ncenters);
kmeans::compute_wcss(
kmat,
ncenters,
res.centers.data(),
res.clusters.data(),
wcss.data()
);If we already allocated arrays for the centroids and clusters, we can fill the arrays directly. This allows us to skip a copy when interfacing with other languages that manage their own memory (e.g., R, Python).
std::vector<double> centers(ndim * ncenters);
std::vector<int> clusters(nobs);
auto deets = kmeans::compute(
kmat,
kmeans::InitializeRandom(), // random initialization
kmeans::RefineHartiganWong(), // refine with Hartigan-Wong
ncenters
centers.data(),
clusters.data()
);If you're using CMake, you just need to add something like this to your CMakeLists.txt:
include(FetchContent)
FetchContent_Declare(
kmeans
GIT_REPOSITORY https://github.com/libscran/kmeans
GIT_TAG master # or any version of interest
)
FetchContent_MakeAvailable(kmeans)Then you can link to kmeans to make the headers available during compilation:
# For executables:
target_link_libraries(myexe ltla::kmeans)
# For libaries
target_link_libraries(mylib INTERFACE ltla::kmeans)By default, this will use FetchContent to fetch all external dependencies.
Applications are advised to pin the versions of each dependency for stability - see extern/CMakeLists.txt for suggested versions.
If you want to install them manually, use -DKMEANS_FETCH_EXTERN=OFF.
To install the library, clone an appropriate version of this repository and run:
mkdir build && cd build
cmake .. -DKMEANS_TESTS=OFF
cmake --build . --target installThen we can use find_package() as usual:
find_package(ltla_kmeans CONFIG REQUIRED)
target_link_libraries(mylib INTERFACE ltla::kmeans)Again, this will automatically acquire all its dependencies, see recommendations above.
If you're not using CMake, the simple approach is to just copy the files in include/ - either directly or with Git submodules - and include their path during compilation with, e.g., GCC's -I.
This requires the external dependencies listed in extern/CMakeLists.txt.
Hartigan, J. A. and Wong, M. A. (1979). Algorithm AS 136: A K-means clustering algorithm. Applied Statistics 28, 100-108.
Arthur, D. and Vassilvitskii, S. (2007). k-means++: the advantages of careful seeding. Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms, 1027-1035.
Su, T. and Dy, J. G. (2007). In Search of Deterministic Methods for Initializing K-Means and Gaussian Mixture Clustering, Intelligent Data Analysis 11, 319-338.