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#clustering — Public Fediverse posts

Live and recent posts from across the Fediverse tagged #clustering, aggregated by home.social.

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  1. @junesim63
    Having been a bit-part player in the #SiliconRoundabout ambition myself, I can say that it was about #digital #entrepreneurs and #startups, #innovation, shared #workspaces and the benefits of multiple companies #clustering in an area rich with #coffee shops, #culture and food.
    It was emphatically not about bulldozing communities to build power-hungry, inhuman industrial monoliths with no connection to their surroundings or to the economic prosperity of the #community.

    Don’t use Silicon Roundabout as an excuse for this vandalism.
    #BrickLane #DataCentre

  2. Ah, the pinnacle of #AI #advancement is upon us: GPT-5.5, now with 50% more #token #clustering confusion 🧩🤖. Apparently, this latest marvel of technology can't tell its reasoning tokens from a hole in the ground, leading to the groundbreaking discovery that more gibberish ≠ better performance. Kudos to #GitHub for hosting the digital equivalent of a toddler trying to assemble a nuclear reactor ⚛️👶.
    github.com/openai/codex/issues #GPT5.5 #TechHumor #HackerNews #ngated

  3. I have been playing with the idea of rewriting an old web-app I made over the past 20 years. It's right now running on #LAMP and is targeted at #mobile devices, but I'd like to modernize it to use #PostgreSQL and a web #framework that's not old enough to be well into its first divorce.

    I'm used to #Java + #Angular (and derivatives like #Ionic), but I'm thinking I want something new. What are people happy with (and have used for actual development beyond a Hello World tutorial)? I can Bing, so I'm more interested in real experiences. I'm not terribly interested in React or Vue, and have already given Swift/SwiftUI for iOS a spin, so I don't want that.

    Should have a mobile-first #frontend, be decently mature so I don't have to rewrite in 2 years when support stops. I need a backend and Postgres access. Would like support for #PWA.

    Can be Angular-based or stand-alone, can be separate backend and frontend or mixed/tag-soup/hydration-like. #Typescript or the like are fine, but not a requirement. Fuck #Erlang / #Elexir. If you're going to suggest #Go or #Rust better have a good argument because most people suggesting those are extremely annoying people. #Flutter / #Dart = yuck.

    I'm using it to track goals/habits and make #charts, #statistics, and some basic machine learning (k-means, affinity propagation, GMM, and other #clustering, that sort of thing). I can program statistics and clustering myself, but a native chart library would be nice (heatmaps, line charts, bar charts/histograms, that sort of thing) and I wouldn't mind not having to implement my own probability distributions. User authentication (persistent between application restarts) is a must (by framework or popular libraries). I can do #Bootstrap, #tailwind, and other front-end #CSS, but I'd be perfectly happy not having to as long as I can make my widgets purple.
  4. Data-driven empathy is the future of UX. Moving beyond the limitations of subjective persona creation, we now utilize clustering algorithms to uncover latent user patterns. This methodological shift ensures design decisions are rooted in empirical evidence rather than intuition. This guide at WebHeads United explores the technical frameworks for this synthesis. Refine your audience insights with algorithmic rigour.

    webheadsunited.com/using-clust

    #DataScience #UXDesign #Clustering #WebDev #Fediverse

  5. GaMAC: Открытая библиотека для автоматической кластеризации мультимодальных данных под GPU

    На сегодняшний день не существует полноценного инструментария для кластеризации на графическом процессоре, что стало основным стимулом для создания универсальной библиотеки, способной автоматически решать задачи кластеризации данных различных представлений. Мы представляем GaMAC - библиотека автоматической оптимизации кластеризации с поддержкой с GPU.

    habr.com/ru/articles/973364/

    #кластеризация #clustering #мультимодальность #автоматическое_машинное_обучение #gpu #мультимодальная_кластеризация

  6. Behind the scenes of “beautiful scientific graphics”

    If you think that clean, elegant visuals in scientific reports are produced in a couple of clicks — I’m here to disappoint you. 🙂

    Here’s one of many failed attempts to visualize results based on thermodynamic modeling of geochemical processes.

    Turning raw multidimensional data into something that a human reader can intuitively grasp is a separate challenge — with dozens of input parameters and only a few truly useful outcomes.

    Good science is not only about computation, but also about communication.

    #Geochemistry #DataViz #Rstats #Clustering #EnvironmentalModelling #ScienceCommunication #PHREEQC #WaterPollution #SvystunovaGully #Contamimation #Groundwater #Infographics #PCA #Thermodynamics

  7. 🧠 New preprint by Chintaluri et al. (2025): An ion channel #omnimodel for standardized #biophysical #neuron #modelling. A unified #HodgkinHuxley formalism applied to >3,500 ion channel models from #ModelDB. Enables cross-model comparison, #clustering, and reproducible simulation through a shared parametrization:

    🌍 doi.org/10.1101/2025.10.03.680

    #CompNeuro #Neuroscience #Reproducibility

  8. Wondering what the implications of the structure and attitude of the #Fediverse are for the small world and the spread of cooperation vs antagonism.
    #networks #clustering
    youtu.be/CYlon2tvywA?si=FKjKO1

  9. 🚀 TopicWatchdog – Week 3: Stable Topics with BERTopic

    KMeans worked, but cluster IDs kept jumping across retrains. This week I added a Python BERTopic stage with a BigQuery registry → stable topic IDs!

    🟢 UMAP + HDBSCAN
    🟢 Stable IDs via registry
    🟢 Auto-labels with Gemini
    🟢 Looker Studio dashboards

    📊 3,802 topics → 2,472 mapped, top clusters: migration, economy, climate, politics.

    👉 Blog: dracoblue.net/dev/topicwatchdo

    #TopicWatchdog #BERTopic #BigQuery
    #Clustering
    #MachineLearning
    #FediScience

  10. Summer ☀️ read: a new paper on model-based clustering just appeared in Computo!

    Julien Jacques and Brendan Thomas Murphy publish a new method for clustering multivariate count data. The method combines feature selection and clustering, and is based on conditionally independent Poisson mixture models and Poisson generalized linear models.

    On simulations, the Adjusted Rand Index (ARI) of the model with selected variables is close to the optimal ARI obtained with the true clustering variables.

    The paper and accompanying R code are available at computo-journal.org/published-

    #machineLearning #clustering #Rstats #openScience #openSource #openAccess

  11. Don't pass by the new insightful lecture from Dr. Alejandro Rodriguez Garcia, Abdus Salam International Centre for Theoretical Physics (ICTP)!

    In this one, Alex provides a comprehensive overview of various clustering methods, including flat, fuzzy, and hierarchical approaches. His lecture not only discusses the mathematical foundations of techniques like k-means and k-medoids but also highlights their practical applications across fields such as image recognition and data classification.

    This lecture is an excellent opportunity to deepen your understanding of unsupervised learning and engage critically with advanced clustering methods.

    Join Enabla to watch the lecture and interact with Dr. Rodriguez Garcia for free! Ask questions and spark discussions with both him and the rest of the Enabla community: enabla.com/pub/1109/about

    #UnsupervisedLearning #MachineLearning #DataScience #Clustering #OpenAccess

  12. Ein lang ersehnter Wunsch von mir: Eigene #Clustering Methoden in #OpenRefine benutzen.

    Verfügbar seit Version 3.9.0 und funktioniert seit 3.9.3 auch mit #Jython und #Clojure.

    Hier eine Anleitung zur Benutzung im #FDMLab Blog.

    fdmlab.landesarchiv-bw.de/work

    #LandesarchivBW

  13. #30DayChartChallenge Día 14: Kinship! 🌿 Hoy toca visualizar "parentescos" animales, pero basados en ¡similitud de rasgos! #RelationshipsWeek #Animals

    Este dendrograma horizontal es el resultado de un clustering jerárquico (hclust Ward.D2) sobre ~170 especies, usando su Masa Corporal y Longevidad Máxima (log-transformadas y escaladas). ¡Muestra quién se agrupa con quién según su estrategia de vida!

    Las ramas unen las especies más similares. La longitud horizontal hasta la unión indica cuán diferentes son. Se ven grandes grupos que separan, por ejemplo, animales muy grandes/longevos de otros más pequeños/rápidos. Es una forma de ver la estructura oculta en los datos de rasgos.

    (Solo se muestra 1/3 de las etiquetas para no saturar!)

    🛠 #rstats #ggplot2 #ggdendro #stats | Datos: Kaggle (S. Banerjee)
    📂 Código/Viz: t.ly/Y_fwt

    #Day14 #Kinship #dataviz #DataVisualization #Ecology #LifeHistory #AnimalTraits #Clustering #Dendrogram #ggplot2 #Kaggle