> ## Documentation Index
> Fetch the complete documentation index at: https://docs.platform.nora.my/llms.txt
> Use this file to discover all available pages before exploring further.

# Clusters

> Signals that share a root cause — fix one, fix many.

Individual signals are noise. Patterns are signal. **Clusters** are how Nora groups related signals so you can fix a class of problem, not a single instance.

## What makes signals cluster

Nora groups signals with:

* Similar **failed intents** — the user asked semantically similar questions.
* Similar **failure modes** — same tool errored, same retrieval missed, same claim contradicted.
* Same **flow / agent** target.
* Same **user segment** (when scope keys allow segmentation).

Clustering is soft — a signal can technically match multiple clusters and Nora picks the best fit. If it fits none well, the signal seeds a new cluster of one.

## Cluster view

For each cluster:

* **Title** — a short human summary Nora generates (editable).
* **Size** — count of signals in the cluster.
* **Trend** — occurrences over time (chart).
* **Root cause hypothesis** — Nora's best guess (editable).
* **Sample signals** — a handful of representative signals to spot-check.
* **Suggested fix** — if the pattern matches a known improvement recipe.

## Cluster confirmation

Confirming a cluster is faster than confirming N individual signals. Click **Confirm cluster** and every signal inside is confirmed at once.

You can also un-cluster: remove a signal from a cluster if it looks misplaced, or split a cluster if two distinct patterns got merged.

## Evolution of clusters

Clusters change over time as new signals arrive:

* **Merge** — two clusters that were separate turn out to be the same underlying issue.
* **Split** — a cluster that mixed two issues gets split when patterns clarify.
* **Grow** — new signals join.
* **Retire** — no new signals for 30 days → cluster archives (still queryable).

The cluster page has a **History** tab showing when signals joined and any splits/merges.

## Cluster priority

Nora ranks clusters by rough estimated impact:

* **Severity × Frequency × Recency** = priority score.
* Adjusted by user importance (if scopes carry a `tier` field, high-tier users boost).
* Adjusted for whether the cluster is a regression (regressions get a boost).

Priority is what determines default ordering in the queue.

## Promoting a cluster

Right-click a cluster → **Create Improvement**. The Improvement flow starts with the full cluster context — every signal, the root cause hypothesis, the sample failures — so proposals are grounded in the pattern, not a single failure.

See [From signal to fix](/reliable/signals/from-signal-to-fix).

## Clusters and datasets

For any confirmed cluster, click **Save as dataset** to turn the signals into test cases:

* Each signal becomes an input-output example.
* Corrections (if any) become the expected outputs.
* The dataset is ready to run simulations against.

This is often the most valuable use of clusters — the failure signals *are* the regression tests.

## Manual clustering

You can manually merge two clusters into one, or move a signal between clusters. Useful when Nora's automatic grouping puts something in the wrong place. Manual moves are learned from — Nora avoids the same misplacement next time.