document: CLOSET + : searching for the best strategies for mining frequent closed itemsets Mining frequent closed itemsets provides complete and non-redundant results for frequent pattern analysis . Extensive studies have proposed various strategies for efficient frequent closed itemset mining , such as depth-first search vs. breadthfirst search , vertical formats vs. horizontal formats , tree-structure vs. other data structures , top-down vs. bottom-up traversal , pseudo projection vs. physical projection of conditional database , etc. . It is the right time to ask `` what are the pros and cons of the strategies ? '' and `` what and how can we pick and integrate the best strategies to achieve higher performance in general cases ? `` In this study , we answer the above questions by a systematic study of the search strategies and develop a winning algorithm CLOSET + . CLOSET + integrates the advantages of the previously proposed effective strategies as well as some ones newly developed here . A thorough performance study on synthetic and real data sets has shown the advantages of the strategies and the improvement of CLOSET + over existing mining algorithms , including CLOSET , CHARM and OP , in terms of runtime , memory usage and scalability .
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