#kmeans
Live, measured metrics for the hashtag #kmeans from the open social web. Every number carries a named source and the time it was fetched. Nothing is estimated.
Own #kmeans
This #name is available to claim. It becomes your portal on the open agent web: this very page, a keyword you rank for by an open public stake, and a verifiable identity for AI agents. Nobody else sells a page like this for every #name.
Day-by-day usage
measured · mas.to (Mastodon public tags API) · fetched 2026-11-09 10:28 UTC0 uses by 0 unique accounts across the window. Real per-day counts, not estimates. Newest bar is today so far.
Related hashtags
measured · mas.to (Mastodon public search API) · fetched 2026-11-09 10:28 UTCLive pulse
measured · mas.to (Mastodon tag timeline) · fetched 2026-11-09 10:28 UTCEverything below is measured over the latest 22 public posts (spanning ~54546 hours).
Posting hours (UTC) — busiest: 13:00
Languages: English (14) · Russian (6) · Spanish (1) · Italian (1)
Avg boosts / post: 0.5
Top of the latest posts
@zefu I find the tool works best for images with a decent contrast and/or color hue range. I also recommend not choosing more than 5-8 colors to avoid too many similar ones. Also bear in mind that k-means clustering relies on random initial
if you're using #kmeans or other clustering algorithms and you use the elbow-method or visual inspection to choose the number of clusters, this paper is for you. https://arxiv.org/abs/2212.12189?utm_source=twitter&utm_medium=social&utm_camp
Хотел обучить нейросеть без backprop, а получил k‑means Хотел проверить, как далеко можно зайти, если учить скрытый слой нейросети вообще без backprop, только локальными хеббовскими правилами. Спойлер: правило в итоге свелось к обычному k‑m
What “kmeans” means
Wikipediak-means clustering is a method of vector quantization, originally from signal processing, that aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean. This results in a partitioning of the data space into Voronoi cells. k-means clustering minimizes within-cluster variances, but not regular Euclidean distances, which would be the more difficult Weber problem: the mean optimizes squared errors, whereas only the geometric median minim
“K-means clustering” on Wikipedia (CC BY-SA) →#kmeans across platforms
every network with a public tag surfaceFollow #kmeans straight to each platform’s own tag page. Where a platform publishes open data we measure it above; the rest lock their numbers behind paid APIs, so we link rather than guess.
Every number above is measured from a named public API at the shown fetch time. Nothing is estimated or extrapolated. Platforms that lock their data behind paid APIs are not shown. Agents: the same numbers, as JSON, at /api/hashtags/kmeans