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blaze @ 1dc3daf

Minicore: Fast Generic Coresets Build Status

Minicore is a fast, generic library for constructing and clustering coresets on graphs, in metric spaces and under non-metric dissimilarity measures. It includes methods for constant-factor and bicriteria approximation solutions, as well as coreset sampling algorithms.

These methods allow for fast and accurate summarization of and clustering of datasets with strong theoretical guarantees.

Minicore both stands for "mini" and "core", as it builds concise representations via core-sets, and as a portmanteau of Manticore and Minotaur.



  1. Boost, specifically the Boost Graph Library.
  2. A compiler supporting C++17. We could remove this requirement without much work.
  3. We conditionally use OpenMP. This is enabled with the setting of the OMP variable.

Certain applications have specific requirements, such as libosmium for kzclustexp, which computes coresets over OpenStreetMaps data, but these are included as submodules primarily.

Python bindings

Python bindings require numpy, scipy, and a recent C++ compiler capable of compiling C++-17.

See python/ for an example and installation instructions, or you can install by running python3 from the base directory.

On some operating systems, SLEEF must be linked dynamically. If python3 install fails, try python3 install, which will build a dynamic library. If you are using Conda, will install the files as necessary, but otherwise you will need to add a directory containing one of these dynamic libraries to LD_LIBRARY_PATH or DYLD_LIBRARY_PATH.

Because minicore compiles distance code for the destination hardware, it's difficult to distribute via PyPI, but can still be installed in a single command via pip:

python3 -m pip install git+git://


  1. graph
    1. Wrappers for boost::graph
  2. coresets
    1. CoresetSampler contains methods for building an importance sampling framework, performing sampling, and reweighting.
    2. IndexCoreset contains a vector of indices and a vector of weights.
    3. Methods for reducing are incomplete, but the software is general enough that this will not be particularly difficult.
      1. Each kind of coreset will likely need a different sort of merge/reduce, as our Coreset only has indices, not the data itself.
    4. MatrixCoreset creates a composable coreset managing its own memory from an IndexCoreset and a matrix.
  3. Approximation Algorithms
    1. k-center (with and without outliers)
    2. k-means
    3. Metric k-median Problem
      1. Local search lsearch.h
      2. Jain-Vazirani jv_solver.h
    4. k,z-clustering using metric k-median solvers, exponentiating the distance matrix by z
    5. [Thorup, 2005]-sampling for pruning search space in both graph shortest-paths metrics and oracle metrics.
  4. Optimization algorithms
    1. Expectation Maximization
      1. k-means
      2. Bregman divergences
      3. L1
        1. weighted median is complete, but it has not been retrofitted into an EM framework yet
  5. blaze-lib row/column iterator wrappers
    1. Utilities for working with blaze-lib
  6. disk-based matrix
    1. Falls back to disk-backed data if above a specified size, uses RAM otherwise.
  7. Streaming metric and \alpha-approximate metric clusterer
    1. minicore/streaming.h
  8. Locality Sensitive Hashing has been extracted into the minilsh library.
    1. LSH functions for:
      1. JSD
      2. S2JSD
      3. Hellinger
      4. L1 distance
      5. L2 distance
      6. L_p distance, 1 >= p >= 2
    2. LSH table
    3. See also DCI for an alternative view on LSH probing.

Soon, the goal is to, given a set of data, k, and a dissimilarity measure, select the correct approximation algorithm, and sampling strategy to generate a coreset, as well as optimize the clustering problem using that coreset.

For Bregman divergences and the squared L2 distance, D^2 sampling works.

For all other measures, we will either use the Thorup-sampled JV/local search approximation method for metrics or the streaming k-means method for alpha-approximate metrics to achieve the approximate solution.

Once this is achieved and importances sampled, we optimize the problems:

  1. EM
    1. Bregman, squared L2, Lp norms - Lloyd's
    2. L1 - Lloyd's, under median
    3. Log-likelihood ratio test, as weighted Lloyd's
  2. General metrics
    1. Jain-Vazirani facility location solver
    2. Local search using swaps

There exist the potential to achieve higher accuracy clusterings using coresets compared with the full data because of the potential to use exhaustive techniques. We have not yet explored this.


graph.h contains a wrapper for boost::adjacency_list tailored for k-median and other optimal transport problems.


kcenter 2-approximation (farthest point)

Algorithm from:

T. F. Gonzalez. Clustering to minimize the maximum intercluster distance. Theoretical Computer Science, 38:293-306, 1985.

Algorithms from:

Hu Ding, Haikuo Yu, Zixiu Wang
Greedy Strategy Works for k-Center Clustering with Outliers and Coreset Construction
  1. kcenter with outiers, 2-approximation
  2. kcenter bicriteria approximation
  3. kcenter with outliers coreset construction (uses Algorithm 2 as a subroutine)


  1. k-means++ initialization scheme (for the purposes of an approximate solution for importance sampling)
  2. k-means coreset construction using the above approximation
  3. Weighted Lloyd's algorithm
  4. KMC^2 algorithm (for sublinear time kmeans++)


  1. Importance-sampling based coreset construction
    1. Note: storage is external. IndexCoreset<IT, FT>, where IT is index type (integral) and FT is weight type (floating point)


MatrixCoreset<MatType, FT> (Matrix Type, weight type (floating point) Constructed from an IndexCoreset and a Matrix, simply concatenates both matrices and weight vectors. Can be reduced using coreset construction.



wrappers in the blz namespace for blaze::DynamicMatrix and blaze::CustomMatrix, with rowiterator() and columniterator() functions allowing range-based loops over the the rows or columns of a matrix.

Norm structs

structs providing distances under given norms (effectively distance oracles), use in kmeans.h


Uses mio for mmap'd IO. Some of our software uses in-memory matrices up to a certain size and then falls back to mmap.


Provides norms and distances. Includes L1, L2, L3, L4, general p-norms, Bhattacharya, Matusita, Multinomial and Poisson Bregman divergences, Multinomial Jensen-Shannon Divergence, and the Multinomial Jensen-Shannon Metric, optionally with priors.


Contains ProbDivApplicator, which is a caching applicator of a particular measure of dissimilarity. Also contains code for generating D^2 samplers for approximate solutions. Measures using logs or square roots cache these values.


The k-center 2-approximation is Gonzalez's algorithm. The k-center clustering, 2-approximation, and coreset with outliers is Ding, Yu, and Wang.

The importance sampling framework we use is from the Braverman, Feldman, and Lang paper from 2016, while its application to graph metrics is from Baker, Braverman, Huang, Jiang, Krauthgamer, and Wu. We also support Varadarajan-Xiao, Feldman Langberg, and Bachem et al., methods for coreset sampling for differing dissimilarity measures.

We use a modified iterative version of the sampling from Thorup paper from 2005 for an initial graph bicriteria approximation, which is described in the above Baker, et al. This can be found for shortest-paths graph metrics and oracle metrics in minicore/bicriteria.h.


Fast and memory-efficient clustering + coreset construction, including fast distance kernels for Bregman and f-divergences.