Ecommerce image duplicate cluster audit calculator

Calculate connected candidate image clusters and candidate cluster count with your own figures.

Your numbers

Use the units shown. Results update as you change valid inputs.

Use connected candidate image clusters and candidate cluster count to compare changes to your inputs. The method and limits below explain the scope.

Examples

Examples

Try the starting example

Images: unique image ID, bound SKUs joined by |, equal-length hexadecimal hash
I1,A,00 I2,B,01 I3,C,03 I4,D,ff
Maximum pairwise Hamming distance for a graph edge
1
Connected candidate image clusters
I1 | I2 | I3
Candidate cluster count
1
Images beyond one representative per cluster
2
Largest within-cluster pair distances
2
Clusters spanning multiple bound SKUs
I1 | I2 | I3

Try another scenario

Images: unique image ID, bound SKUs joined by |, equal-length hexadecimal hash
I1,A,00 I2,B,01 I3,C,03 I4,D,ff
Maximum pairwise Hamming distance for a graph edge
0
Connected candidate image clusters
None
Candidate cluster count
0
Images beyond one representative per cluster
0
Largest within-cluster pair distances
None
Clusters spanning multiple bound SKUs
None
Calculation method

Calculation method

Connected candidate image clusters

Join image nodes when supplied hashes differ by at most the threshold; report connected components larger than one.

Candidate cluster count

Count connected components with two or more images.

Images beyond one representative per cluster

Sum(cluster size−1), not a deletion recommendation.

Largest within-cluster pair distances

Report maximum actual pair distance within each connected cluster, which can exceed the edge threshold through transitive links.

Clusters spanning multiple bound SKUs

Report candidate clusters whose combined binding set contains multiple SKUs.

Symbols and units
a
Images: unique image ID, bound SKUs joined by |, equal-length hexadecimal hash
b
Maximum pairwise Hamming distance for a graph edge

Use the units shown. A list keeps the order in which you enter its values. The symbol ^ means “raised to the power of”.

Assumptions & limits

Assumptions & limits

  • A supplied-hash graph audit, without image generation, visual inspection or file deletion. Hash closeness and transitive membership are candidate signals, not proof of identical products or images.
Are these inputs private?

These calculations run in your browser. A shared link contains the values you choose to share. Only questions submitted to the separate AI finder are sent to OpenAI.

Further reading

Google Merchant Center: product-data terminology and consistency background ↗

Background reading; this does not imply endorsement or independent review of SumFizz.

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