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.
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。
There were problems in traditional parallel algorithms for mining frequent itemsets more or less: data deviation, large scale communication, frequent synchronization and scanning database.
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。
The existing association rules mining algorithms are chiefly based on frequent itemsets, and the record about infrequent itemsets is very rare.
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。
Mining maximum frequent itemsets is a key problem in many data mining application.
挖掘最大频繁项目集是多种数据挖掘应用中的关键问题 。
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.
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.
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。
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