itemsets

itemsets

美  英

  • 网络项目集;项集合;项目组

例句

Experimental results show that this algorithm improves the speed of frequent itemsets mining.

实验结果显示,该算法提高了频繁项集的挖掘速度。

After calculating the minimum support of each item set, frequency itemsets meeting the user specified minimum support are selected.

计算各候选项集的支持度后,得到满足最小支持度的频繁项集;

An association model is made up of a series of itemsets and the rules that describe how those items are grouped together within the cases.

关联模型由事例中一系列项集和说明这些项如何分组的规则组成。

Mining frequent closed itemsets is a fundamental and important issue in many data mining applications.

频繁闭项集挖掘是许多数据挖掘应用中的重要问题。

The experiment result shows that the improved Agorithm can effectively mine the frequent itemsets on the dense dataset and long patterns.

实验结果表明,经改进后的Apriori算法能够有效地挖掘密集数据集和长模式下的频繁项集。

Tab displays the list of itemsets that the model identified as frequently found together.

选项卡显示被模型识别为经常发现一起出现的项集的列表。

Discovering maximum frequent itemsets is a key problem in many data mining applications.

发现最大频繁项目集是多种数据开采应用中的关键问题。

The other is to produce strong association rules by frequent itemsets.

由频繁项集产生强关联规则。

Specifies the maximum number of itemsets to produce.

指定要生成的最大项集数。

Discovering frequent itemsets is important in data mining association rules.

发现频繁项目集是关联规则数据挖掘中的关键问题。

In order to generate association and dependency relation between concepts, join and prune phases are used to extract potential itemsets.

为得到概念间关联依赖规则,合并和修剪技术用于产生候选的项集;

Association rules mining is finished by two steps. One is to find all frequent itemsets.

关联规则的挖掘由两个步骤来完成:一、找出所有频繁项集;

Mining frequent itemsets is a basic task of the data stream mining.

挖掘频繁项集是挖掘数据流的基本任务。

DS(deep search)-ECLAT algorithm is a new frequent itemsets algorithm proposed on the fundation of ECLAT algorithm.

在ECLAT算法的基础上,提出一种新的频繁项集挖掘算法——DS-ECLAT算法。

Finding all frequent itemsets is the first step of association rule mining.

产生频繁集是产生关联规则的第一步。

Due to the limitations, the paper presents a frequent itemsets mining algorithm which is based on the frequent pattern tree.

针对这些不足论文提出一种基于频繁模式树的频繁项集挖掘算法。

At the same time, it stores the frequent itemsets in a two indexes link.

同时,把得到的频繁项集存储到一个双层索引链表中。

Study of Incremental Rapid Maintenance Algorithm for Frequent Itemsets

增量式频集快速维护算法研究

Incremental Updating Algorithm for Mining Constrained Maximum Frequent Itemsets

约束最大频繁项目集的增量式更新算法

Updating Technique for Frequent Itemsets in Distributed Database System in the Case Minimal Support Change

最小支持度变化时分布式数据库中频繁项目集更新

Privacy preserving distributed data mining association rules of frequent itemsets

分布式环境下保持隐私的关联规则挖掘

Interactive Visualization of Frequent Itemsets and Association Rules

一种交互式关联规则和频繁项集的可视化方法

Text Classification Using Sentential Frequent Itemsets

使用句子级频繁项目集的文本分类方法

Optimization algorithm of fast and effectively mining maximum frequent itemsets

一种挖掘最大频繁项目集的优化算法

Incremental updating algorithm for mining maximum frequent itemsets

最大频繁项目集的增量式更新算法

An Algorithm and Its Updating Algorithm Based on Frequent Pattern Tree for Mining Constrained Maximum Frequent Itemsets

一种基于频繁模式树的约束最大频繁项目集挖掘及其更新算法

An Algorithm for Mining Maximum Frequent Itemsets without Candidate Generation

一种不产生候选集的最大频繁集快速挖掘算法

Fast algorithm for discovering maximum frequent itemsets of association rules

关联规则最大频繁项目集的快速发现算法

Incremental Mining Large Itemsets with Constraints in Dynamic Databases

带限制的动态数据库中大项目集增量式挖掘

Algorithm for mining constrained maximal frequent itemsets

基于约束的最大频繁项集挖掘算法

A Sequence-based Algorithm for the Fast Discovery of Maximum Frequent Itemsets

一种快速发现最大频繁项目集的序列算法

Fast Mining Algorithm for Distributed Global Frequent Itemsets

分布式全局频繁项目集的快速挖掘方法

Improved Algorithm of Association Rules for Frequent Multi-itemsets

针对高项频繁集的关联规则改进算法

Research on Methods of Concise Representation for Frequent Itemsets

频繁项集的简洁表示方法研究

Logic "and" operation algorithms in finding frequent itemsets

生成频繁项集的逻辑“与”运算算法

Commentary About Technology of Mining Frequent Itemsets

频繁项集挖掘技术述评

Research on Maximum Frequent Itemsets Mining Technology

最大频繁项目集挖掘技术研究

Parallel Algorithm for Mining Maximal Frequent Itemsets

挖掘最大频繁项集的并行算法

An Improved FP-Tree-Based Algorithm for Maximal Target Frequent Itemsets Mining

一种改进的基于FP-树的最大目标频繁项集挖掘算法

Algorithm of Produce Frequent Itemsets and Interest Measure

常项集产生的算法及兴趣度量