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Many parallelization techniques have been proposed to enhance the performance of the Apriori-like frequent itemset mining algorithms. Characterized by both map and reduce functions, MapReduce has emerged and excels in the mining of datasets of terabyte scale or larger in either homogeneous or heterogeneous clusters. Minimizing the scheduling overhead of(More)
Sequential pattern mining is a challenging issue because of the high complexity of temporal pattern discovering from numerous sequences. Current mining approaches either require frequent database scanning or the generation of several intermediate databases. As databases may fit into the ever-increasing main memory, efficient memory based discovery of(More)
Sequential patterns in customer transactional databases are commonly mined for E-Commerce recommendations. In many practical applications, the absence of certain item-sets and sequences could have important implications. Mining frequent sequences comprising not only the occurrence but also the absence of certain sequences will increase the accuracy of(More)
BACKGROUND The pathophysiological mechanisms of renal function progression in chronic kidney disease (CKD) have still not been completely explored. In addition to well-known traditional risk factors, non-traditional risk factors, such as endothelial dysfunction, have gradually attracted physicians' attention. Angiopoietin-2 (Ang-2) impairs endothelial(More)
Current approaches for sequential pattern mining usually assume that the mining is performed in a static sequence database. However, databases are not static due to update so that the discovered patterns might become invalid and new patterns could be created. In addition to higher complexity, the maintenance of sequential patterns is more challenging than(More)