Frequent patterns are often used for discovery of several types of knowledge such as association rules, episode rules, sequential patterns, and clusters. Since the number of frequent itemsets is usually huge, several lossless representations have been proposed. Frequent closed itemsets and frequent generators are the most useful representations from application point of view. Discovery of closed itemsets requires prior discovery of generators. Generators however are usually discovered directly from the data set. In this paper we will prove experimentally that it is more beneficial to compute the generators representation in two phases: 1) by extracting the generalized disjunction-free generators representation from the database, and 2) by transforming this representation into the frequent generators representation. The respective algorithm of transitioning from one representation to the other is proposed.