Use this probabilistic structure when bounded memory and fast approximate membership, cardinality, or similarity queries are preferable to retaining every original item.

left

Required list input.

right

Required list input.

hash Count

Required integer input.

band Count

Required integer input.

Use banded MinHash signatures to flag a pair of string sets as a similarity candidate. A matching band is not proof of semantic similarity. The tool evaluates the supplied inputs together and returns the named outputs below; it does not infer omitted operating conditions or change the units shown.^[1]

candidate

The resulting candidate returned as a boolean.

matching Bands

The resulting matching bands returned as a list.

• Probabilistic structures trade exactness for space. Error rates depend on capacity, hash quality, parameter choice, and merge compatibility; some structures permit false positives, and most cannot reconstruct the inserted values.
• Use finite inputs in the displayed units and preserve more precision than the final presentation requires. Independently verify safety-critical, financial, compliance, or production decisions.

Choose parameters from the expected cardinality and acceptable error rate, monitor saturation, and retain an exact store when results must be verified. Merge only structures created with compatible parameters and hashing.

• MinHash Jaccard Similarity Estimator

  Compare two sets of strings using seeded 32-bit MinHash signatures. Estimated Jaccard can differ from exact Jaccard, especially with few hashes.

• Counting Bloom Filter Calculator

  Build a deterministic counting Bloom filter, apply requested removals, and test a query. Positive membership and multiplicity remain probabilistic because hash collisions can overestimate both.

• HyperLogLog Cardinality Estimator

  Estimate the number of distinct strings with a fixed-size HyperLogLog sketch. The estimate is probabilistic and may differ from the exact count.