项目集

标准API可用于设置并从项目集获得数据。

Standard APIs can be used to set and get data from the item set .

例如，在保存到项目集以前，布尔值应该为字符串值true或false。

For example , a boolean value should be string value true or false before being saved to the item set .

通过挖掘free项目集来挖掘关联规则已被证明是一种十分高效的方法。

Ming of free sets has proved to be an efficient way for association rule mining .

基于候选项目集特性的改进Apriori算法研究

Research on Candidate Items Based on Improved Apriori Algorithm

saveItemSet函数接受JSON对象并将其值存储到项目集。

The_saveItemSet function accepts a JSON object and stores its value into the item set .

频繁项目集发现算法Apriori的研究

Research on frequent item sets discovery algorithm Apriori

好处是JSON对象比项目集更容易使用。

The benefit is that a JSON object is much easier to use than the item set .

项目集是持久性存储，iWidget可用它来存储用户首选项和内部数据。

Item set is persistence storage that an iWidget can use to store user preferences and internal data .

Fp-growth算法是当前挖掘频繁项目集算法中速度最快，应用最广，并且不需要候选集的一种挖掘关联规则的算法。

Fp-growth algorithm is one of the currently fastest and most popular algorithms for mining association rule without candidate generation .

数据挖掘算法Apriori在处理关联规则分析时，当项目集很多、数据立方体很稠密时回溯的次数急剧增加。

When the data mining algorithm Apriori processes association rules analysis , with more item-set and dense cube , the time of trace has a sharp increase .

在基于位串数组的数据挖掘算法的基础上，进一步提出了一种快速的基于位串数组的最大频繁项目集挖掘算法（BSAMFIA）。

Based on the association rule mining algorithm with bit string array , a fast algorithm for mining maximum frequent itemsets with bit string array ( BSA-MFIA ) is proposed .

针对基于Apriori的算法需要多次访问数据库并产生大量候选项目集的缺点，提出一种基于分类树的算法。

In order to overcome disadvantages of Apriori based algorithms that need to access database many times and produce a lot of candidate item-sets , a classification tree based algorithm is designed .

在把每个事务中的频繁项目集插入到FP-tree的过程中，采用动态指针来实现，提高存储空间利用率。

Each transaction in the frequent item sets insert into the process of FP-tree , the dynamic pointer to achieve , which can improve storage utilization . 2 .

然后运用有限概念格与Rough集理论相结合形成Rough有限概念格，蕴涵规则则由其特有的上、下近似运算得到，不需计算繁琐的频繁项目集。

The constrained concept lattice , together with the rough set theory , is then incorporated into the method to implement a new restricted rough lattice based implication rules discovery ( RRLIRD ) approach to interactively acquire the rules with the specific rough upper and lower approximation .

本文充分论述了典型的频繁项目集发现算法Apriori算法、AprioriTid算法，及已有的对于频繁项目集发现算法相关的改进措施的优缺点。

In the thesis we fully describe the most typical discovery algorithms of the frequent item sets , Apriori algorithm and Apriori_Tid algorithm , and discuss the advantages and the disadvantages of some existent improved methods .

传统更新算法与Apriori算法框架一致，要多遍扫描数据库并产生大量的候选项目集。

Conventional maintenance algorithms employ same framework as Apriori . However , candidate set generation is still costly , and the algorithms need repeatedly scan the database , especially when there exist prolific patterns and / or long patterns .

本文通过研究项目集治理的组织结构、辅助信息系统的功能模型，以及对项目集生命周期中的管理活动进行探讨，形成了一套具体而又完整的BSS项目集管理方案。

A set of concrete and comprehensive program management solutions is explored in this paper through studies on the organizational structure of program management , the function model of supporting information system , and the management activities in the life cycle of program .

生产批量计划问题是MRPⅡ（制造资源计划）的一个关键问题，它研究一定计划期内，如何将产品的生产批量和时间在加工项目集上进行分配，并满足一定的性能指标集。

Lot sizing problem , which addresses how to distribute the part batches and time periods over some producing items during the determining time horizon , and subject to some performance index sets , plays a key role in implementing MRP ⅱ( Manufacturing Resource Planning ) .

实验表明改进后算法比FP-Growth算法具有更好的性能。然后，在数据库记录增加的情况下，提出了一种高效的最大频繁项目集的增量更新问题。

Experiments show that the time and space for the improved algorithm have reduced significantly compared to FP-Growth mining . Then , Increase in the case of database records , a Maximum frequent item-sets of the most efficient incremental update problem .

本文分析了高效的FP-growth算法在共享存储体系结构下，并行建立频繁模式树和并行挖掘频繁项目集的实现方法，指出了算法存在由于任务分配不均而导致处理器之间负载不均衡的缺陷。

The other class of algorithms finds the associations without candidacy . And based on the efficient FP-growth algorithm , its implementation method of constructing the frequent pattern tree and mining frequent item sets is given for the shared memory parallel formulation .

为此，本文提出运用MFIA-IU算法来解决数据库和最小支持度同时变化时的综合更新挖掘最大频繁项目集的问题，从而可避免每年对旧数据的重复挖掘。

So the MFIA-IU algorithm is proposed to mine comprehensive updated maximum frequent sets when the database and the minimal support change simultaneously . Thereby , the annually repeated mining of the old data is avoided .

最后得到频繁项目集和关联规则。

Finally the frequent item sets and association rules were found .

频繁项目集及相关事务集的挖掘算法

An Algorithm Mining Frequent Item Set and their Related Transaction Set

项目集管理的九大知识领域和五个管理过程组。

Nine knowledge areas and five phases for Program Management .

关联规则中最大频繁项目集的研究

Study for Mining Maximally Frequent Item Sets in Association Rule

建立频繁项目集向量的极大频繁项目集挖掘

Maximal frequent item set mining with establishment of frequent item set vectors

阅读本文后面有关项目集的更多信息。

Read more about item set later in this article .

基于项目集的关联规则数据挖掘

Date mining of associated rules based on item sets

项目集本身非常易于使用。

Item set itself is very simple to use .

该酒店项目集住宿、餐饮、娱乐于一体。

The hotel is set accommodation , catering , entertainment projects in one .