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。
Many approximation algorithms behave well in frequent items mining, but can not control their memory consumption.
数据流频繁项挖掘算法需要利用有限的内存，以尽量少的次数扫描数据流就能得到频繁项。
Frequent item mining algorithms need to perform as little data stream scanning as possible while using limited size of memory.
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。
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.
实验表明，该算法对于频繁项集挖掘具有比较高的效率。
The experiments show that FP-DFS has good efficiency in frequent item-set mining.
现有关联规则挖掘算法都是在频繁项集基础上进行挖掘，关于非频繁项集的资料很少。
The existing association rules mining algorithms are chiefly based on frequent itemsets, and the record about infrequent itemsets is very rare.
传统的挖掘频繁项集的并行算法存在数据偏移、通信量大、同步次数较多和扫描数据库次数较多等问题。
There were problems in traditional parallel algorithms for mining frequent itemsets more or less: data deviation, large scale communication, frequent synchronization and scanning database.
算法在产生了频繁1-项集之后，分别利用1-项集中的项作为约束条件，建立压缩FP-树，挖掘跨事务关联规则。
After the frequent 1-itemsets is produced, it separately uses them as constraint conditions to construct compact FP-tree and to mine inter-transactional association rules.
频繁项集是挖掘流数据挖掘的基本任务。
Mining frequent items is a basic task in stream 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。
The mining of frequent items in transactional database is an important task of data mining.
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。
Besides, tree structure is extensively adopted in data mining because it doesn't need to generate the frequent items and test them.
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。
Mining most frequent K items in data streams means finding K items whose frequencies are larger than other items in data streams.
该方法克服了传统关联规则挖掘方法的不足，在产生频繁项集的同时进行规则挖掘，从而提高了挖掘效率。
This method conquers the disadvantage of traditional association rules mining methods, mining rules while mining frequent-item set, so the mining efficiency is greatly enhanced.
采用项集格生成树的数据结构，将最大频繁项集挖掘过程转化为对项集格生成树进行深度优先搜索获取所有最大频繁节点的过程。
The itemset lattice tree data structure was adopted to translate maximal frequent itemsets mining into the process of depth-first searching the itemset lattice tree.
因此提出了最大支持度的概念，用来约束频繁项集的挖掘，排除没有意义的关联规则同时也提高了挖掘的效率。
Therefore a concept named maximum support is introduced, which is used to bind the frequent items mined, and exclude meaningless association rules.
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。
On this basis, the algorithm of generating frequent items from transformed data sets is proposed.
针对频繁闭项集挖掘算法中数据结构与处理机制复杂的问题，提出窗口快速滑动的数据流频繁闭项集挖掘算法——MFWSR。
This paper proposes an algorithm of Mining Frequent closed itemsets with Window Sliding Rapidly(MFWSR) against the complexity of data structure and process for determination.
本文介绍一种更适合于关系型数据库关联规则挖掘的、基于SQL的频繁项关联规则挖掘算法，并将其应用于教学评价。
This paper discusses how to mine data in a way that based on SQL language and making it fit for the relation database better, so it is used in teaching evaluation.
发现频繁项集是关联规则挖掘的主要途径，也是关联规则挖掘算法研究的重点。
Discovering frequent item sets is the main way of association rules mining, and it is also the focus of the study in algorithms for association rules mining.
发现频繁项集是关联规则挖掘的主要途径，也是关联规则挖掘算法研究的重点。
Discovering frequent item sets is the main way of association rules mining, and it is also the focus of the study in algorithms for association rules mining.
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