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The goal of Frequent Item set Mining (FIM) is to find the biggest number of frequently used subsets from a big transaction database. In previous studies, using the advantage of multicore computing, the execution time of an Apriori algorithm was sharply decreased: when the size of a data set was more than TBs and a single host had been unable to afford a large number of operations by using a number...
The mining association rule is an important research field in data mining. The mining association rule usually adopts this model: support, confidence, interestingness. But this model can't measure the correlative degree between the antecedent and the consequent of the rule by ration. So we proposed a new mining model of association rules: support, coincidence, interestingness and analyzed the meaning...
Association rule mining is an important research field in data mining. Current association-rule methods mainly depend on the support-confidence framework. The strategy is not quite effective in consideration of the time-sensitive factor and correlation problem between antecedent and consequent of rules. To solve this problem, a novel association rules framework has been proposed: time-validity support...
Because of the exponential growth in worldwide information, companies have to deal with an ever growing amount of digital information. One of the most important challenges for data mining is quickly and correctly finding the relationship between data. The Apriori algorithm is the most popular technique in association rules mining; however, when applying this method, a database has to be scanned many...
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