【摘要】：大數(shù)據(jù)環(huán)境下高效用項(xiàng)集挖掘算法中過(guò)多的候選項(xiàng)集極大地降低了算法的時(shí)空效率,為此提出了一種減少候選項(xiàng)集的數(shù)據(jù)流高效用項(xiàng)集挖掘算法。通過(guò)數(shù)據(jù)流中當(dāng)前窗口的一次掃描建立一個(gè)全局樹(shù),并降低全局樹(shù)中頭表入口與節(jié)點(diǎn)的冗余效用值�；谌謽�(shù)生成候選模式,基于增長(zhǎng)算法降低局部樹(shù)的候選項(xiàng)集效用,從候選模式中選出高效用模式�；谡鎸�(shí)數(shù)據(jù)流的實(shí)驗(yàn)結(jié)果表明,算法的時(shí)空效率與內(nèi)存占用比均優(yōu)于其他數(shù)據(jù)流的高效用模式挖掘算法。
[Abstract]:In big data environment, too many candidate itemsets in the mining algorithm of high-utility itemsets greatly reduce the space-time efficiency of the algorithm. Therefore, a data stream high-utility itemset mining algorithm for reducing candidate itemsets is proposed. A global tree is established by scanning the current window in the data stream, and the redundant utility value of the header table entry and node in the global tree is reduced. The candidate pattern is generated based on the global tree and the candidate set utility of the local tree is reduced based on the growth algorithm, and the high utility pattern is selected from the candidate pattern. Experimental results based on real data flow show that the space-time efficiency and memory occupancy ratio of the algorithm are better than those of other data streams.
【作者單位】： 新鄉(xiāng)學(xué)院計(jì)算機(jī)與信息工程學(xué)院;河南大學(xué)計(jì)算機(jī)與信息工程學(xué)院;
【基金】：河南省科技廳軟科學(xué)研究計(jì)劃資助項(xiàng)目(152400410345);河南省科技廳科技攻關(guān)資助項(xiàng)目(172102210445) 河南省教育廳資助項(xiàng)目(15A520093)
【分類(lèi)號(hào)】：TP311.13

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本文編號(hào)：2257698