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k-means

Algorithms AIMech:KMeans DRAFT

Clustering that partitions observations around centroids by minimizing within-cluster squared distances.

Identity Notes & Concept Scope The Clustering task does not specify the centroid/squared-distance objective of this algorithm.

Claims & Grounded Evidence

1 Claim

Mechanism #mechanism

The fitting procedure alternates assignment to the nearest centroid and centroid updates from assigned observations.

supports Source: scikit-learn User Guide: clustering · Locator: User Guide, section #k-means

This record supports method identity and the stated mechanism only; no universal performance or biological efficacy claim is curated.
Proposed placement is an inference from the existing AIO hierarchy; maintainers must resolve task, algorithm and model scope before acceptance.

Mapped AIO Classes

AIO Target Class	Mapping Relation	Mapping Rationale
`AIO:Clustering` Clustering	related_match	The Clustering task does not specify the centroid/squared-distance objective of this algorithm. Mapping is local curation, not an upstream subclass assertion.

1 Source

Source ID	Publication / Document	Type / Access	Reference & Checksum
`method-source`	scikit-learn User Guide: clustering scikit-learn developers	official documentation documentation	sha256:323e56d3d77e…

Curator: Codex

Method: ai_assisted

Model: GPT-6

Created: 2026-10-11T03:01:34Z

Updated: 2026-10-11T03:01:34Z

Source File: data/curated/KMeans.yaml