CAJ | 학술논문

根据数据流的特点,提出了一种挖掘约束频繁闭合项集的算法,该算法将数据流分段,用DSCFCI_tree动态存储潜在约束频繁闭合项集,对每一批到来的数据流,首先建立局部DSCFCI_tree,进而对全局DSCFCI_tree进行有效更新并剪枝,从而有效地挖掘整个数据流中的约束频繁闭合模式.实验表明,该算法具有很好的时间和空间效率.
근거수거류적특점,제출료일충알굴약속빈번폐합항집적산법,해산법장수거류분단,용DSCFCI_tree동태존저잠재약속빈번폐합항집,대매일비도래적수거류,수선건립국부DSCFCI_tree,진이대전국DSCFCI_tree진행유효경신병전지,종이유효지알굴정개수거류중적약속빈번폐합모식.실험표명,해산법구유흔호적시간화공간효솔.
According to the characteristics of data streams,a new algorithm was proposed for mining constrainted frequent closed patterns.The data stream was divided into a set of segments,and a DSCFCI_tree was used to store the potential constrainted frequent closed patterns dynamically.With the arrival of each batch of data,the algorithm first built a corresponding local DSCFCI_tree,then updated and pruned the global DSCFCI_tree effectively to mine the constrainted frequent closed patterns in the entire data stream.The experiments and analysis show that the algorithm has good performance.