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Cache-conscious frequent pattern mining on modern and emerging processors
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Clicks: An effective algorithm for mining subspace clusters in categorical datasets
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SQUIRE: Sequential pattern mining with quantities
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Discovery of maximum length frequent itemsets
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Optimization of frequent itemset mining on multiple-core processor
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A time- and memory-efficient frequent itemset discovering algorithm for association rule mining
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A data mining proxy approach for efficient frequent itemset mining
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A survey on algorithms for mining frequent itemsets over data streams
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An efficient mining algorithm for maximal weighted frequent patterns in transactional databases
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Fast algorithms for maximal clique enumeration with limited memory
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A Contribution to the Use of Decision Diagrams for Loading and Mining Transaction Databases
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On commutativity based edge lean search
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Mining co-locations under uncertainty
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We present GenMax, a backtrack search based algorithm for mining maximal frequent itemsets. GenMax uses a number of optimizations to prune the search space.It usesa novel technique called progressive focusing to perform maximality checking, and diffset propagation to perform fast frequency computation. Systematic experimental comparison with previous work indicates that different methods have varying strengths and weaknesses based on dataset characteristics. We found GenMax to be a highly efficient method to mine the exact set of maximal patterns.