Methods › General › Clustering › k-Means Clustering

k-Means Clustering

772 papers tagged archive 2025-07-28

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

k-Means Clustering is a clustering algorithm that divides a training set into k different clusters of examples that are near each other. It works by initializing k different centroids {μ(1),…,μ(k)} to different values, then alternating between two steps until convergence:

(i) each training example is assigned to cluster i where i is the index of the nearest centroid μ⁽ⁱ⁾

(ii) each centroid μ⁽ⁱ⁾ is updated to the mean of all training examples x⁽ʲ⁾ assigned to cluster i.

Text Source: Deep Learning, Goodfellow et al

Image Source: scikit-learn

The archive's code-snippet link for this entry is not shown: it points outside the code hosts this site links to, at a host listed as spam. It is counted among the archive links this site does not show.

Papers archive 2025-07-28

30 shown of 772, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

20 shown of 446 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Clustering353
Object Detection162
object-detection150
Object84
Semantic Segmentation41
Segmentation39
General Classification36
Classification34
Autonomous Driving26
Image Classification24
image-classification23
BIG-bench Machine Learning21
Deep Learning21
Image Segmentation20
Data Augmentation19
Quantization19
Real-Time Object Detection18
GPU17
Self-Supervised Learning17
Time Series17

Usage over time archive 2025-07-28

Papers per year tagged with k-Means Clustering: 2006 to 2025, peak 131 131 0 2006: 1 paper 2006 2007: 0 papers 2008: 0 papers 2008 2009: 0 papers 2010: 1 paper 2010 2011: 1 paper 2012: 0 papers 2012 2013: 5 papers 2014: 6 papers 2014 2015: 15 papers 2016: 23 papers 2016 2017: 20 papers 2018: 27 papers 2018 2019: 61 papers 2020: 102 papers 2020 2021: 115 papers 2022: 131 papers 2022 2023: 110 papers 2024: 101 papers 2024 2025: 53 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (772 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Clustering

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