pith:VFS2QMAB
CAKE: Confidence in Assignments via K-partition Ensembles
CAKE assigns each clustering point a score in [0,1] by combining its stability across multiple k-partition runs with the consistency of its geometric fit to the assigned cluster.
arxiv:2602.18435 v2 · 2026-02-20 · cs.LG
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Record completeness
Claims
The theoretical analysis shows that CAKE remains effective under noise and separates stable from unstable points.
That an ensemble of k-partitions generated by an initialization-sensitive algorithm such as k-means will produce stability and geometric-fit statistics that meaningfully reflect true assignment reliability rather than artifacts of the chosen distance metric or initialization distribution.
CAKE produces per-point confidence scores in [0,1] for clustering assignments by combining cross-run stability with local geometric consistency over an ensemble of k-partitions.
Receipt and verification
| First computed | 2026-05-17T23:38:59.911407Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
a965a830012ca2c0de3cf606f8f53ae549d168222aaec9f756f57e3da549b337
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/VFS2QMABFSRMBXR46YDPR5J24V \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: a965a830012ca2c0de3cf606f8f53ae549d168222aaec9f756f57e3da549b337
Canonical record JSON
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