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Efficient Mining Algorithm of Frequent Itemsets for Uncertain Data Streams

机译:不确定数据流频繁项集的高效挖掘算法

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With the rapid development of computer technology, web services has been widely used. In these applications, the uncertain data is in the form of streams. In view of this kind of situation, present a new generalized data structure, that is, PSUF - tree, to store uncertain data streams, all itemsets in recent window are contained in global PStree in a condensed format, establish a header table in which contains dynamic array of expected value whose header table saved the same itemsets. Based on PSUF-tree, present a new mining algorithm for frequent itemsets, that is, PSUF-streaming algorithm, frequent itemsets could be mined by traversing the dynamic array, the maintaining of PSUF-tree just handles the header table corresponds to the oldest batch of itemsets in window. The experimental results show that PSUF-streaming algorithm has good efficiency and scalability, and reduce memory usage to some extent.
机译:随着计算机技术的飞速发展,Web服务已被广泛使用。在这些应用中,不确定数据以流的形式出现。针对这种情况,提出了一种新的通用数据结构,即PSUF-树,用于存储不确定的数据流,最近窗口中的所有项目集都以压缩格式包含在全局PStree中,建立一个包含期望值的动态数组,其标头表中保存了相同的项目集。在PSUF-tree的基础上,提出了一种新的频繁项集挖掘算法,即PSUF-streaming算法,可以通过遍历动态数组来挖掘频繁项集,PSUF-tree的维护只是处理头表对应最早的一批窗口中的项目集。实验结果表明,PSUF流算法具有良好的效率和可扩展性,并在一定程度上减少了内存使用。

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