## Use a sampling process to generate candidate sets

## Randomly select subset of transactions to consider

## Negative Border, Bd-(S) : Set of minimal itemset sets X where X is not in S

## Scan D using S union Bd-(S) as candidate set

## Expand by adding negative border recursively until negative border empty

## Worse case - 2 passes, Best case - 1 pass

## First scan may be very inefficient

## Small non-zero probability of missing itemset, for complete accuracy use entire database

## With Partitioning, may view 1st partition as sample

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