document: Fast vertical mining using diffsets A number of vertical mining algorithms have been proposed recently for association mining , which have shown to be very effective and usually outperform horizontal approaches . The main advantage of the vertical format is support for fast frequency counting via intersection operations on transaction ids ( tids ) and automatic pruning of irrelevant data . The main problem with these approaches is when intermediate results of vertical tid lists become too large for memory , thus affecting the algorithm scalability . In this paper we present a novel vertical data representation called Diffset , that only keeps track of differences in the tids of a candidate pattern from its generating frequent patterns . We show that diffsets drastically cut down the size of memory required to store intermediate results . We show how diffsets , when incorporated into previous vertical mining methods , increase the performance significantly .
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