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Clustering datasets

archive 2025-07-28

5 datasets carry the task tag "Clustering" (the task itself: Clustering), ordered by the archive's paper count. Page 1 of 1: 5 shown of 5. Facet routes are this site's own (the archive records the tag string, not a page).

The archive holds 12,214 dataset rows; 12,172 are listed. 6 are withheld from every listing and count here as vandalised before snapshot (6 with contact-centre spam in the title, 0 with a spam description on a row that has no homepage, no paper and no papers counted; none with more than 1 paper, 0 with a benchmark), listed in withheld.json; 1 listed row carries a vandalised description, withheld on its page. This gate never withholds a row with a homepage or a paper that resolves, and a clean description; the content rules below withhold a row whose name is spam whatever else it carries. The gate is a phrase list: these are the rows it caught, not a claim that the rest is clean. Before that gate, the site's content rules withhold 36 more rows (invite-code, gambling, travel-booking, contact-centre and similar spam in the name or on a row with nothing real behind it); they have no page and are listed in withheld.json.

Filter 51 task tags shown of 3,717, by dataset count; the full filter by modality, task and language is on /datasets

Clustering datasets 1–5 of 5

TIMIT (TIMIT Acoustic-Phonetic Continuous Speech Corpus)
The TIMIT Acoustic-Phonetic Continuous Speech Corpus is a standard dataset used for evaluation of automatic speech recognition systems.
31 papers · 6 benchmarks
The dataset comprises motion sensor data of 19 daily and sports activities each performed by 8 subjects in their own style for 5 minutes.
1 paper · 0 benchmarks
Click to add a brief description of the dataset (Markdown and LaTeX enabled).
1 paper · 0 benchmarks
MapReader Data (in GeoHumanities workshop, SIGSPATIAL 2022)
MapReader in GeoHumanities workshop (SIGSPATIAL 2022): Gold standards and outputs Refer to: https://github.com/Living-with-machines/MapReader/wiki/GeoHumanities-workshop-in-SIGSPATIAL-2022
1 paper · 0 benchmarks
VocSim (Vocal Similarity Benchmark)
VocSim (Vocal Similarity Benchmark) is a benchmark designed to evaluate the ability of neural audio embeddings to capture acoustic and perceptual similarity in a zero-shot setting, without task-specific fine-tuning.
0 papers · 0 benchmarks

Paper counts and descriptions are the archive's, frozen 2025-07-28; no citation counts, no stars, no trending. Sorting by "most cited" or "newest" was a live-site feature the archive does not carry.