Patterned ZnO nanowires were successfully synthesized on ITO electrodes deposited on the glass substrate by using a simple thermal evaporation approach.The morphology,crystallinity and optical properties of ZnO nanowi...Patterned ZnO nanowires were successfully synthesized on ITO electrodes deposited on the glass substrate by using a simple thermal evaporation approach.The morphology,crystallinity and optical properties of ZnO nanowires were characterized by scanning electron microscopy,X-ray diffraction,energy dispersive X-ray and pho-toluminescence spectroscopy.Their field emission characteristics were also investigated.SEM images showed that the ZnO nanowires,with a diameter of 100-200 nm and length up to 5μm,were highly uniform and well distributed on the linear ITO electrodes.The field emission measurement indicated that patterned ZnO nanowire arrays have a turn-on field of 1.6 V/μm at current density of 1μA/cm~2 and a threshold field of 4.92 V/μm at current density of 1 mA/cm~2 at an emitter-anode gap of 700μm.The current density rapidly reached 2.26 mA/cm~2 at an applied field of 5.38 V/μm.The fluctuation of emission current was lower than 5%for 4.5 h.The low turn-on field,high current density and good stability of patterned ZnO nanowire arrays indicate that it is a promising candidate for field emission application.展开更多
现有的基于滑动窗口挖掘高效用项集的研究方法存在:候选项集通常数量巨大,需要大量的存储空间及计算候选项集的真实效用是非常耗时的问题。本文提出一种不生成候选项集的挖掘算法HUISW(high utility itemset mining over a siding windo...现有的基于滑动窗口挖掘高效用项集的研究方法存在:候选项集通常数量巨大,需要大量的存储空间及计算候选项集的真实效用是非常耗时的问题。本文提出一种不生成候选项集的挖掘算法HUISW(high utility itemset mining over a siding window),HUISW采用一种新的树结构HUIL-Tree(high utility itemset tee which arranges items according to lexicographic order)存储滑动窗口中的项集信息,采用效用数据库存储项集在窗口事务中的效用信息,在挖掘过程中HUISW采用模式增长的方法对由HUIL-Tree生成的项集通过其与效用数据库的对应关系,直接计算其在滑动窗口中的效用,整个过程避免了候选项集的生成。在实验中通过由稀疏和稠密数据集模拟的数据流对HUISW进行性能评估,并与同类算法SHU-Growth(siding window based high utility growth)进行比较,实验结果表明HUISW显著优于SHU-Growth,运行时间最快可提升两个数量级。展开更多
Mining high-utility itemsets （HUIs） from a transaction database refers to the discovery of itemsets with high utilities like profits. Most of existing studies discover HUIs from a transaction database in two phases....Mining high-utility itemsets （HUIs） from a transaction database refers to the discovery of itemsets with high utilities like profits. Most of existing studies discover HUIs from a transaction database in two phases. In phase 1, different overestimation methods are applied to calculate the upper bounds of the utilities of itemsets. Since the overestimated utilities of itemsets are adopted, the itemsets whose overestimated utilities are no less than a user-specified threshold are selected as candidate HUIs, and they are verified by scanning the database one more time in phase 2. However, a large number of candidate HUIs incur two problems： 1） it requires excessive memory to store these candidates; 2） it needs a large amount of running time to calculate their exact utilities. Vertical data format has been applied to mine HUIs recently. However this kind of method cannot deal with transactions with the same items effectively so that the size of database cannot be reduced sufficiently. The overall performance of algorithms is degraded consequently. Thus an algorithm HUITWU is proposed in this paper for mining HUIs. A novel data structure HUITwu-Tree is adopted to efficiently calculate the utilities of itemsets in a database. Extensive studies with both sparse and dense datasets have demonstrated that our proposed algorithm is more than an order of magnitude faster and consumes less memory than the state-of-the-art algorithms.展开更多
如何在海量数据集中提高频繁项集的挖掘效率是目前研究的热点.随着数据量的不断增长,使用传统算法产生频繁项集的计算代价依然很高.为此,提出一种基于Spark的频繁项集快速挖掘算法(fast mining algorithm of frequent itemset based on ...如何在海量数据集中提高频繁项集的挖掘效率是目前研究的热点.随着数据量的不断增长,使用传统算法产生频繁项集的计算代价依然很高.为此,提出一种基于Spark的频繁项集快速挖掘算法(fast mining algorithm of frequent itemset based on spark,Fmafibs),利用位运算速度快的特点,设计了一种新颖的模式增长策略.该算法首先采用位串表达项集,利用位运算来快速生成候选项集;其次,针对超长位串计算效率低的问题,考虑将事务垂直分组处理,将同一事务不同组之间的频繁项集通过连接获得候选项集,最后进行聚合筛选得到最终频繁项集.算法在Spark环境下,以频繁项集挖掘领域基准数据集进行实验验证.实验结果表明所提方法在保证挖掘结果准确的同时,有效地提高了挖掘效率.展开更多
针对基于模式增长原理的嵌入式子树挖掘算法——TreeGrowth(TG)算法挖掘子树过大与内存消耗大缺点,在分区挖掘思想的基础上,提出了一种新算法——PTG(partition tree growth)算法。PTG算法将数据库划分成多个分区,先用TG算法进行挖掘,...针对基于模式增长原理的嵌入式子树挖掘算法——TreeGrowth(TG)算法挖掘子树过大与内存消耗大缺点,在分区挖掘思想的基础上,提出了一种新算法——PTG(partition tree growth)算法。PTG算法将数据库划分成多个分区,先用TG算法进行挖掘,得到每个分区的局部频繁子树。根据全局支持数进行筛选,得到全局频繁子树,有效地减少了挖掘的子树,有效地降低了内存的开销。仿真实验结果表明,PTG算法能够解决在大数据集上挖掘时出现内存空间不足的问题,验证了其有效性与健壮性。展开更多
基金supported by the National High Technology Research and Development Program for Advanced Materials of China(No. 2008AA03A313)the Technology Projects of Department of Education,Fujian Province,China(Nos.JA09017,JA11014)
文摘Patterned ZnO nanowires were successfully synthesized on ITO electrodes deposited on the glass substrate by using a simple thermal evaporation approach.The morphology,crystallinity and optical properties of ZnO nanowires were characterized by scanning electron microscopy,X-ray diffraction,energy dispersive X-ray and pho-toluminescence spectroscopy.Their field emission characteristics were also investigated.SEM images showed that the ZnO nanowires,with a diameter of 100-200 nm and length up to 5μm,were highly uniform and well distributed on the linear ITO electrodes.The field emission measurement indicated that patterned ZnO nanowire arrays have a turn-on field of 1.6 V/μm at current density of 1μA/cm~2 and a threshold field of 4.92 V/μm at current density of 1 mA/cm~2 at an emitter-anode gap of 700μm.The current density rapidly reached 2.26 mA/cm~2 at an applied field of 5.38 V/μm.The fluctuation of emission current was lower than 5%for 4.5 h.The low turn-on field,high current density and good stability of patterned ZnO nanowire arrays indicate that it is a promising candidate for field emission application.
文摘现有的基于滑动窗口挖掘高效用项集的研究方法存在:候选项集通常数量巨大,需要大量的存储空间及计算候选项集的真实效用是非常耗时的问题。本文提出一种不生成候选项集的挖掘算法HUISW(high utility itemset mining over a siding window),HUISW采用一种新的树结构HUIL-Tree(high utility itemset tee which arranges items according to lexicographic order)存储滑动窗口中的项集信息,采用效用数据库存储项集在窗口事务中的效用信息,在挖掘过程中HUISW采用模式增长的方法对由HUIL-Tree生成的项集通过其与效用数据库的对应关系,直接计算其在滑动窗口中的效用,整个过程避免了候选项集的生成。在实验中通过由稀疏和稠密数据集模拟的数据流对HUISW进行性能评估,并与同类算法SHU-Growth(siding window based high utility growth)进行比较,实验结果表明HUISW显著优于SHU-Growth,运行时间最快可提升两个数量级。
基金This work is partly supported by the National Basic Research 973 Program of China under Grant No. 2012CB316200 and the National Natural Science Foundation of China under Grant Nos. 61190115 and 61173022.
文摘Mining high-utility itemsets （HUIs） from a transaction database refers to the discovery of itemsets with high utilities like profits. Most of existing studies discover HUIs from a transaction database in two phases. In phase 1, different overestimation methods are applied to calculate the upper bounds of the utilities of itemsets. Since the overestimated utilities of itemsets are adopted, the itemsets whose overestimated utilities are no less than a user-specified threshold are selected as candidate HUIs, and they are verified by scanning the database one more time in phase 2. However, a large number of candidate HUIs incur two problems： 1） it requires excessive memory to store these candidates; 2） it needs a large amount of running time to calculate their exact utilities. Vertical data format has been applied to mine HUIs recently. However this kind of method cannot deal with transactions with the same items effectively so that the size of database cannot be reduced sufficiently. The overall performance of algorithms is degraded consequently. Thus an algorithm HUITWU is proposed in this paper for mining HUIs. A novel data structure HUITwu-Tree is adopted to efficiently calculate the utilities of itemsets in a database. Extensive studies with both sparse and dense datasets have demonstrated that our proposed algorithm is more than an order of magnitude faster and consumes less memory than the state-of-the-art algorithms.
文摘如何在海量数据集中提高频繁项集的挖掘效率是目前研究的热点.随着数据量的不断增长,使用传统算法产生频繁项集的计算代价依然很高.为此,提出一种基于Spark的频繁项集快速挖掘算法(fast mining algorithm of frequent itemset based on spark,Fmafibs),利用位运算速度快的特点,设计了一种新颖的模式增长策略.该算法首先采用位串表达项集,利用位运算来快速生成候选项集;其次,针对超长位串计算效率低的问题,考虑将事务垂直分组处理,将同一事务不同组之间的频繁项集通过连接获得候选项集,最后进行聚合筛选得到最终频繁项集.算法在Spark环境下,以频繁项集挖掘领域基准数据集进行实验验证.实验结果表明所提方法在保证挖掘结果准确的同时,有效地提高了挖掘效率.
文摘针对基于模式增长原理的嵌入式子树挖掘算法——TreeGrowth(TG)算法挖掘子树过大与内存消耗大缺点,在分区挖掘思想的基础上,提出了一种新算法——PTG(partition tree growth)算法。PTG算法将数据库划分成多个分区,先用TG算法进行挖掘,得到每个分区的局部频繁子树。根据全局支持数进行筛选,得到全局频繁子树,有效地减少了挖掘的子树,有效地降低了内存的开销。仿真实验结果表明,PTG算法能够解决在大数据集上挖掘时出现内存空间不足的问题,验证了其有效性与健壮性。
