Mining frequent items is a basic task in stream data mining.
频繁项集是挖掘流数据挖掘的基本任务。
The mining of frequent items in transactional database is an important task of data mining.
挖掘事务库中的频繁项集是数据挖掘的重要任务之一。
Mining most frequent K items in data streams means finding K items whose frequencies are larger than other items in data streams.
数据流最频繁K项挖掘是指在数据流中找出K个项，它们的支持数大于数据流中的其他项。
Besides, tree structure is extensively adopted in data mining because it doesn't need to generate the frequent items and test them.
此外，由于树结构在挖掘频繁项目时不需要产生频繁项集及对这些频繁项进行测试而被广泛应用于数据挖掘中。
It proposes a new database store structure AFP-Tree for mining frequent patterns, makes recommendations by exploring associations between items, exemplifies the approach on real data.
提出了一个新的数据库存储结构AF P -树，利用它来挖掘频繁模式。然后利用项目之间的相互关联做出推荐。最后举例说明了此推荐系统的处理过程。
Many approximation algorithms behave well in frequent items mining, but can not control their memory consumption.
许多近似算法能够有效进行频繁项挖掘，但不能有效控制内存资源消耗。
Frequent items mining is a very basic but important task in the data stream processing.
频繁项集挖掘是一个非常基本的，但最重要的任务，在数据流处理。
The result indicates that we can remarkably decrease the candidate items and improve the efficiency of mining frequent pattern when using the interest measure.
分析结果表明，利用规则兴趣度能够大大减小候选项目集的大小，有效提高频繁模式挖掘算法的效率。
We need search after the new methods of mining association rules, so as to avoid several bugs of frequent items.
为了避免频集方法的一些缺陷，我们需要探索挖掘关联规则的新方法。
A frequent items mining algorithm of stream data (SW-COUNT) was proposed, which used data sampling technique to mine frequent items of data flow under sliding Windows.
提出了一种流数据上的频繁项挖掘算法(SW - COUNT)。该算法通过数据采样技术挖掘滑动窗口下的数据流频繁项。
This paper also discusses how to set the optimal minimum support for the common association rules mining algorithm, which can guarantee the frequent items are the weighted frequent items' superset.
同时，给出了最优的最小支持度设定方法，保证了普通关联规则算法所产生的频繁集为加权频繁集的超集。
This algorithm first produces frequent items using previous common association rules mining algorithm, then produces weighted frequent items from the previous frequent items.
该算法首先利用普通的关联规则算法产生频繁集，然后在该频繁集的基础上产生加权频繁集。
This algorithm first produces frequent items using previous common association rules mining algorithm, then produces weighted frequent items from the previous frequent items.
该算法首先利用普通的关联规则算法产生频繁集，然后在该频繁集的基础上产生加权频繁集。
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