Many data mining problems require the discover of frequent patterns in order to be solved. Frequent Itemsets are useful in the discover 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. In the paper, we describe three basic loassless representations of frequent patters 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 the determining the new representation.