Stream data is a very common data type in big data and in many data streams applications,users tend to pay more attention to the mode information of the data *** mining frequent patterns in data streams is a significa...
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ISBN:
(纸本)9781479900763
Stream data is a very common data type in big data and in many data streams applications,users tend to pay more attention to the mode information of the data *** mining frequent patterns in data streams is a significative ***,finding frequent itemests in a data set with predefined fixed support threshold could be seen as an optimization *** this paper,the problem of frequent itemsets mining is derived as a non-linear optimization problem,then genetic algorithm is adopted to solve *** the formal and bitmap representation of frequent itemsets,the non-linear optimization problem is transformed to 0-1 programming.A set of experimental results show that unlike typical Apriori algorithm,the complexity of time and memory space grows exponentially as the support decrease,our proposed algorithm has a high time and space efficiency even with a very low support.
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