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The ability to extract frequent pairs from a set of transactions is one of the fundamental building blocks of data mining. When the number of items in a given transaction is relatively small the problem is trivial. Even when dealing with millions of transactions it is still trivial if the number of unique items in the transaction set is small. The problem becomes much more challenging when the authors deal with millions of transactions, each containing hundreds of items that are part of a set of millions of potential items. Especially when they are looking for highly correlated results at extremely low support levels.
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