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Vector Quantization (k-means problem)

5 papers with code · 0 benchmarks · 0 datasets archive 2025-07-28

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Given a data set X of d-dimensional numeric vectors and a number k find a codebook C of k d-dimensional vectors such that the sum of square distances of each x ∈X to the respective nearest c ∈C is as small as possible. This is also known as the k-means problem and is known to be NP-hard.

Description from the archive archive 2025-07-28.

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Most implemented papers archive 2025-07-28

5 shown of 5 papers with code (5 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

  • Breathing K-Means: Superior K-Means Solutions through Dynamic K-Values
    28 Jun 2020 2 repositories listed
    This superior performance was maintained even when comparing the best result of ten runs for all other algorithms to a single run of breathing k-means, highlighting its effectiveness and speed.
  • Fast K-Means with Accurate Bounds
    8 Feb 2016 2 repositories listed
    We propose a novel accelerated exact k-means algorithm, which performs better than the current state-of-the-art low-dimensional algorithm in 18 of 22 experiments, running up to 3 times faster.
  • Data Aggregation for Hierarchical Clustering
    5 Sep 2023 1 repository listed
    Hierarchical Agglomerative Clustering (HAC) is likely the earliest and most flexible clustering method, because it can be used with many distances, similarities, and various linkage strategies.
  • The Effect of Points Dispersion on the k-nn Search in Random Projection Forests
    25 Feb 2023 1 repository listed
    k-nn search in an rpForest is influenced by two factors: 1) the dispersion of points along the random direction and 2) the number of rpTrees in the rpForest.
  • Learning the k in k-means
    1 Dec 2003 1 repository listed
    The G-means algorithm is based on a statistical test for the hypothesis that a subset of data follows a Gaussian distribution.

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