Many data mining problems require the discovery of frequent patterns in order to be solved. Frequent itemsets are useful in the discovery of association rules, episode rules, sequential patterns and clusters. The number of frequent itemsets is usually huge. Therefore, it is important to work out concise representations of frequent itemsets. We describe three basic lossless representations of frequent patterns in a uniform way and offer a new lossless representation of frequent patterns based on disjunction-free generators. The new representation is more concise than two of the basic representations and more efficiently computable than the third representation. We propose an algorithm for determining the new representation.
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