Unsupervised learning finds structure in data that has no target, through clustering, density estimation or dimensionality reduction.
What the base framework establishes
The approach works without a labelled answer. The model looks for groups, patterns or a compressed representation in the data it is given. Because there is no target, the framework's evaluation logic cannot use the same measures as supervised learning; the result is judged by how useful or stable the structure is, and that judgement needs to be recorded. Clustering and dimensionality reduction are named in the task clause, so the two clauses are read together. The clause text is not in the public sample, and this page does not pretend to quote it.
Why it matters to an AI management system
Unsupervised output is often treated as a fact about the data rather than a result of choices about distance, features and the number of groups. A governance record should name those choices, because a different choice produces a different picture and sometimes a different decision about customers or risk.
The public draft summary names no change to 7.3 Unsupervised machine learning. The final text could still touch it.
Evidence a reviewer samples
| Category | Artefact |
|---|---|
| Method record | The distance measure, the number of groups and the features used. |
| Stability check | Whether the structure holds under a different seed or sample. |
| Use note | How the output is used and what a human must confirm before acting. |
One question to prepare for. If you changed the distance measure, would the groups still be the same?
Common gaps
- The number of clusters is chosen for presentation convenience and never tested.
- The output is treated as a label and enters a decision without human review.
- The feature set is not recorded, so the result cannot be reproduced.
Prepare before publication
Record the method and run one stability check. Add a use note that says what a human must confirm before the output changes a decision.
Dates that matter
No separate date attaches to 7.3; the record dates are the stage date, the planning estimate and the CEN expectation stated above.
Cross-references
6.2 Task · 7.1 General · 8.4 Modelling
Source
Source for 7.3: ISO/IEC 23053:2022, clause 7.3. The public sample stops before 7.3, so this page relies on the public contents and the public summary and does not quote the clause. The two official records are linked on this page so any line can be checked.
Not legal advice; does not certify. A reading of the framework and the evidence a reviewer commonly samples, not a substitute for the standard or your auditor's rules.