The second, clustered the data, and then discovered frequent items sets in the result of clustering.
然后对数据先进行聚类，再在聚类结果中发掘频繁项目集；
Due to the irreversibility of random hash mapping, current sketch data structures have to traverse the key address space to find frequent items.
由于随机哈希函数不可逆，目前的概要数据结构不得不遍历关键字地址空间以查找和估计频繁项集。
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 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。
Applying the hierarchical sketch, an algorithm that finds hierarchical frequent items over data streams dynamically and approximately was implemented.
应用该多层概要数据结构，实现了面向数据流的多层频繁项集的动态近似查找算法。
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。
On this basis, the algorithm of generating frequent items from transformed data sets is proposed.
在此基础上，给出了在伪装后的数据集上生成频繁项集的挖掘算法。
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。
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。
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。
应用推荐