频繁

基本解释 (translations)

frequence

与频繁相关的网络例句 [注：此内容来源于网络，仅供参考]

The new algorithm constructs an association graph to represent the frequent relationship between items, and recursively generates frequent closed itemsets based on that graph.

GRG算法构造关联图代表频繁项之间的频繁关系，并递归地从关联图中产生频繁闭项集。

Aiming at the multiple loop structures in quality management process network, an improved graph pattern mining approach based on Apriori algorithm is put forward, where a frequent pattern of size k, denoted as G, is joined with another frequent pattern of size k, denoted as G, to form a candidate frequent pattern of size (k+1), if there exists a source vertex s in G and a sink vertex e in G such that the graph derived from G by subtracting node s equals with the graph derived from G G by subtracting node e.

采用基于APriori方法的频繁活动序列挖掘算法，认为k-频繁图集中，当一个图减去其中的一个源顶点后，如果所得到的图与另一个图减去其中的一个沉顶点后的图相同时，可以连接生成一个(k+1)-候选频繁图，从而减少了传统Apriori算法迭代过程中生成的冗余候选频繁图的数目。

At each database sites, sampling algorithm and constrained Eclat algorithm are implemented. And the local frequent itemsets satisfying constraints are developed. They then are combined to global frequent itemsets that satisfying constraints based on learning from induction.

算法在各数据站点上对一个较小的样本采用基于约束的Eclat类算法挖掘局部约束频繁项集，用归纳学习的方法归并所有局部约束频繁项集产生全局约束频繁项集。

Then, considering the knowledge represented by a set of frequent patterns, we investigate the problems of preserving sensitive patterns in data sharing, hiding sensitive patterns in frequent pattern sharing, and also blocking inference channels in frequent pattern sharing. The main contributions are as follows:(1) For the problem of preserving anonymity in data sharing, we propose a clustering-based approach for implementing l-diversity.

本文首先研究了数据共享中匿名保护问题，接着以频繁模式为知识的表现形式，研究了数据共享中的敏感模式保护，频繁模式共享中的敏感模式隐藏，以及频繁模式共享中的推理控制等问题，主要的研究成果如下：(1)针对数据共享中的匿名保护问题，提出了一种基于聚类的l-多样化实现方法。

Then, considering the knowledge represented by a set of frequent patterns, we investigate the problems of preserving sensitive patterns in data sharing, hiding sensitive patterns in frequent pattern sharing, and also blocking inference channels in frequent pattern sharing.

本文首先研究了数据共享中匿名保护问题，接着以频繁模式为知识的表现形式，研究了数据共享中的敏感模式保护，频繁模式共享中的敏感模式隐藏，以及频繁模式共享中的推理控制等问题，主要的研究成果如下：（1）针对数据共享中的匿名保护问题，提出了一种基于聚类的l-多样化实现方法。

The set of frequent closed patterns determines exactly the complete set of all frequent patterns and is usually much smaller than the laster. Yet mining frequent closed patterns remains to be a memory and time consuming task.

频繁闭合模式集惟一确定频繁模式完全集并且尺寸小得多，然而挖掘频繁闭合模式仍然是时间与存储开销很大的任务。

So, this paper, based on mathematical expectation, brought up the concept of the positively correlated frequent itemsets, and improved the algorithm of mining frequent itemsets directly in FP-tree to mine positively correlated frequent itemsets. In this way, a solution to the above mentioned problems was got.

针对以上问题，基于数学期望，提出了正相关的频繁项集的概念，并改进了一种直接在FP-树中挖掘频繁项集的算法，挖掘出正相关的频繁项集，从而有效地解决以上问题。

An efficient algorithm based on FP-tree and support array for mining maximal frequent itemsets is proposed. At first the FP-tree and the support array are created at the same time. Then a maximal frequent itemsets tree MAXFP-tree is built up to store all the maximal frequent itemsets. Therefore, it can reduce the search space and improve the efficiency of the algorithm.

提出了一个基于频繁模式树即FP-tree和支持度数组相结合的最大频繁项集挖掘算法，首先建立FP-tree，同时建立支持度数组，然后在此基础上建立最大频繁项集树MAXFP-tree，MAXFP-tree中包含了所有最大频繁项集，缩小了搜索空间，提高了算法的效率。

Since any frequent itemset is a subset of a maximal frequent itemset (an itemset is maximal frequent if it has no superset that is frequent), this paper proposes the DMFI (Discovery of Maximal Frequent Itemsets) algorithm for mining all the maximal frequent itemsets from data sets. This algorithm searches the maximal frequent itemsets in data sets from both bottom-up and top-down directions in the meantime. This paper proposes an algorithm that evaluates the boundaries on the support and confidence of uncalculated itemsets by exploiting the information provided by the calculated itemsets.

本文在经典关联规则的基础上，提出了一系列扩展的关联规则开采算法：发现关联规则的难度体现在发现频繁项目集上，事实上最大频繁项目集（其所有的超集都为非频繁项目集的频繁项目集）的集合已经包含了所有的频繁项目集，本文提出一种发现最大频繁项目集的算法DMFI(Discovery Maximal Frequent Itemsets)，该算法采用自底向上和自顶向下相结合的搜索策略对数据空间进行有效的搜索。

The algorithm first takes count of the browse number of each access sequence by overlapping operation,then unites and deletes the unfrequent page items according to minimum support degree given by users,afterwards sifts getting the intersections of each two user access pattern and gives birth to candidate grequent access patterns,at last,adds up the number of each candidate frequent access pattern by scanning the original database.

该算法首先对用户项集进行重叠操作统计浏览次数，然后合并，依据用户给出的最小支持度删除原项集中的非频繁页面元素，并对两两用户项集筛选生成候选频繁项集，最后扫描数据库，统计各个候选频繁项集的支持度计数。

与频繁相关的网络解释 [注：此内容来源于网络，仅供参考]

commercial lending：频繁性贷款

频繁项目顺差favorable balance of current account, surplus of current account | 频繁性贷款 commercial lending | 频繁性支出 running expenses

frequent：频繁

不久,就总是不停,频繁(frequent)地打电话来了,最后竟然上瘾了. frequent时常发生的, 频繁的,通常强调动作的多次反复出现;B. regular有规则的,经常的,强调动作有规律的经常出现;unusual不平常的,不平常的,强调非常的情况;

frequent itemset：频繁项目集

本文提出了多层次关联规则的挖掘算法--MLIG,利用向量"或"和"与"运算求解频繁项目集(Frequent Itemset),该算法通过构建向量之间的关系矩阵,将频繁项目集的产生过程转化为项目集的关系矩阵中向量运算过程,大大提高了挖掘的效率和速度.

frequent itemset：频繁项集

通常关联规则是从频繁项集(Frequent Itemset)中提取的. 可以通过更改概念格结构形成频繁封闭项集格,并采用不同的方法获得格节点所对应的同交易项集中的最小项集集合(th...

frequent itemset：频繁项目表

频繁项目集:Frequent Itemset. | 频繁项目表:frequent-itemset | 频繁交往:Frequent interaction

frequent itemset：频繁集

常用词:Frequent words | 频繁集:Frequent Itemset | 频繁元素:frequent item

Strong Lyrics：直接和频繁的在歌词中涉及性、暴力、酒精或毒品

.Strong Language:直接和频繁的出现亵渎的语言 | .Strong Lyrics:直接和频繁的在歌词中涉及性、暴力、酒精或毒品 | .Strong Sexual Content-画面涉及或描述可能包含裸体的性行为

Strong language：直接和频繁的出现亵渎的语言

.Some Adult Assistance May Be Needed:可能需要家长辅助,适合低龄儿童 | .Strong Language:直接和频繁的出现亵渎的语言 | .Strong Lyrics:直接和频繁的在歌词中涉及性、暴力、酒精或毒品

Frequent episodes：频繁序列

频繁项目集:frequent itemset | 频繁序列:Frequent episodes | 频繁项集:frequent itemset

Frequent episodes：频繁情节

频繁集项:Frequent itemsets | 频繁情节:frequent episodes. | 频繁项目集:Frequent Itemset.