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growth算法的
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  “growth算法的”译为未确定词的双语例句
     Research on Implementation of the FP-growth Algorithm
     FP-growth算法的实现方法研究
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     Research and Application of Association Rules Mining Based on Fp-growth Algorithm
     基于Fp-growth算法的关联规则挖掘算法研究和应用
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     The article also introduces and analyzes several improved algorithms such as AprioriTid and AprioriHash, and an improved algorithm of FP-growth algorithm - PFP-growth as well.
     文章还介绍并分析了Apriori算法的几种改进算法AprioriTid和AprioriHash等,同时还介绍并分析了FP-growth算法的改进算法PFP-growth。
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     On the other hand, the new algorithm is compared with FP-Growth in the performance.
     同时本文也给出了通过编程测试此算法和FP-Growth算法的性能,并作比较。
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     One Way of FP-growth Algorithm Implementation
     FP-growth算法的一种优化实现
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  相似匹配句对
     Research on Implementation of the FP-growth Algorithm
     FP-growth算法的实现方法研究
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     An improvement to FP growth agorithm
     FP-growth算法的一种改进
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     The Determination of Algorithm
     算法的确定性
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     A Study of G-Algorithm
     G算法的研究
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     (TGF-?) .
     (Transforming growth factor-???TGF-?)
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  growth algorithm
Research and application on Web information retrieval based on improved FP-growth algorithm
      
An advanced incremental crack growth algorithm for the three-dimensional (3D) simulation of fatigue crack growth in complex 3D structures with linear elastic material behavior is presented.
      
Finite element crack growth algorithm for dynamic fracture
      
A new finite element crack growth algorithm has been developed to simulate dynamic fracture.
      
Previously a three-dimensional fatigue crack growth algorithm has been incorporated into the alternating method for the case of constant amplitude loading.
      
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Constrained association rule mining is an important method for integrating Apriori knowledge, improving efficiency of algorithms, and reducing size of discovered rules in the association rule mining. The existing algorithms for mining constrained association rules are all based on the Apriori algorithm, whose performance will decline on dense databases. In this paper a novel algorithm for mining constrained association rules, FPC, which is based on a new type algorithm for association rule mining, FP Growth,...

Constrained association rule mining is an important method for integrating Apriori knowledge, improving efficiency of algorithms, and reducing size of discovered rules in the association rule mining. The existing algorithms for mining constrained association rules are all based on the Apriori algorithm, whose performance will decline on dense databases. In this paper a novel algorithm for mining constrained association rules, FPC, which is based on a new type algorithm for association rule mining, FP Growth, was proposed. The FPC algorithm takes advantage of the stepwise method of the FP Growth algorithm for generating frequent patterns to construct a constraint tree to check whether the generated frequent patterns satisfy the constraints, and then prune those that do not satisfy the constraints. Experiments comparing the FPC with the existing algorithm, Reorder, showed that the former runs an order faster than the latter.

项约束关联规则发现是在关联规则发现中加入先验知识、提高算法执行效率、精简所发现的规则数量的重要方法 .现有的项约束关联规则发现算法都基于 Apriori算法框架 ,在高密度数据库上的执行性能不佳 ,而且没有提出高效的约束条件检验方法 .在一种新型高效关联规则发现算法 FP- Growth的基础上 ,提出了一种全新的项约束关联规则发现算法 FPC.FPC算法利用 FP- Growth算法逐步生成高频项集的方式 ,构造了一种约束树数据结构 ,及时检查高频项集满足约束条件的情况 ,尽早删去不满足约束的条件 .实验证明 ,此算法执行效率比 Reorder等基于 Apriori的算法高一个数量级 .

Mining frequent itemsets is a major aspect of data mining researches. Efficient mining algorithm research of frequent itemsets is the key problem. Conventional Apriori and its improved algorithms need to generate a lot of candidate sets, and repeatedly scan the database, especially when there exist prolific patterns and /or long patterns. In order to overcome the drawbacks of Apriori algorithm, Han J W et al. proposed the FP growth algorithrn. The algorithm only scans ransaction database twice, with no need...

Mining frequent itemsets is a major aspect of data mining researches. Efficient mining algorithm research of frequent itemsets is the key problem. Conventional Apriori and its improved algorithms need to generate a lot of candidate sets, and repeatedly scan the database, especially when there exist prolific patterns and /or long patterns. In order to overcome the drawbacks of Apriori algorithm, Han J W et al. proposed the FP growth algorithrn. The algorithm only scans ransaction database twice, with no need to generate any candidate sets. But FP growth algorithm only generates conditional pattern bases and conditional pattern trees of one item each time, so the efficiency is affected. In this paper, we introduce F-matrix, and propose an improved algorithm of FP growth—IFP growth. Experimental result shows that IFP growth algorithm efficiently improves FP growth.

频繁项目集挖掘是多种数据挖掘应用研究的一个重要方面,频繁项目集的快速挖掘算法研究是当前研究的热点.传统Apriori及其改进算法.要多遍扫描数据库并产生大量的候选项目集.针对Apriori算法的缺陷,HanJW(韩家炜)提出FP-growth算法,该算法仅须扫描数据库两遍且无须生成候选项目集,避免了产生“知识的组合爆炸”,提高了频繁项目集的挖掘效率.但FP-growth算法是通过逐步生成条件模式基和条件频繁模式树来挖掘频繁项目集.因而影响了频繁项目集的挖掘效率.为此,在引入F-矩阵概念之后,提出了FP-growth的一种改进算法—IFP-growth,并对改进算法的性能进行了测试.实验结果表明,IFP-growth算法优于FP-growth.

FP-growth algorithm is one of the most efficient frequent pattern mining methods published recently. However, FP-growth algorithm must generate a huge number of conditional FP-trees recursively in process of mining, so the efficiency of FP-growth remains unsatisfactory. In this paper, the structure of a traditional FP-tree is improved and an efficient frequent pattern-mining algorithm based on constrained sub-tree is proposed. The new FP-tree is a one-way tree and there is no pointers to point its children in...

FP-growth algorithm is one of the most efficient frequent pattern mining methods published recently. However, FP-growth algorithm must generate a huge number of conditional FP-trees recursively in process of mining, so the efficiency of FP-growth remains unsatisfactory. In this paper, the structure of a traditional FP-tree is improved and an efficient frequent pattern-mining algorithm based on constrained sub-tree is proposed. The new FP-tree is a one-way tree and there is no pointers to point its children in each node, so at least one third of memory is saved compared with the former structure in the same storage of frequent pattern information. By introducing constrained sub-tree (consisting of three small arrays), the proposed algorithm doesn't generate conditional FP-trees in mining process and therefore greatly improves the mining efficiency in both time and space. Experiments show that in comparison with FP-growth, this algorithm has accelerated the mining speed by at least two times and reduced the space consumption by half. Moreover, the algorithm has a very good time and space scalability with the number of transactions, and has an excellent performance in dense database mining as well.

FP growth算法是目前已发表的最有效的频繁模式挖掘算法之一 然而 ,由于在挖掘频繁模式时需要递归地生成大量的条件FP 树 ,其时空效率仍然不够高 改进了FP 树结构 ,提出了一种基于被约束子树挖掘频繁项集的有效算法 改进的FP 树是单向的 ,每个结点只保留指向父结点的指针 ,这大约节省了三分之一的树空间 通过引入被约束子树(可以用 3个很小的数组表示 ) ,算法在挖掘频繁模式时不生成条件FP 树 ,从而大大提高了频繁模式挖掘的时空效率 实验表明 ,与FP growth算法相比 ,算法的挖掘速度提高了 1倍以上 ,而所需的存储空间减少了一半 此外 ,随着数据库规模的增大 ,算法具有很好的可伸缩性 对于稠密数据集 ,算法也具有良好的性能 .

 
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