document: Mining frequent item sets by opportunistic projection In this paper , we present a novel algorithm Opportune Project for mining complete set of frequent item sets by projecting databases to grow a frequent item set tree . Our algorithm is fundamentally different from those proposed in the past in that it opportunistically chooses between two different structures , array-based or tree-based , to represent projected transaction subsets , and heuristically decides to build unfiltered pseudo projection or to make a filtered copy according to features of the subsets . More importantly , we propose novel methods to build tree-based pseudo projections and array-based unfiltered projections for projected transaction subsets , which makes our algorithm both CPU time efficient and memory saving . Basically , the algorithm grows the frequent item set tree by depth first search , whereas breadth first search is used to build the upper portion of the tree if necessary . We test our algorithm versus several other algorithms on real world datasets , such as BMS-POS , and on IBM artificial datasets . The empirical results show that our algorithm is not only the most efficient on both sparse and dense databases at all levels of support threshold , but also highly scalable to very large databases .
doc_bio_tags: O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O