It has smaller orders of magnitude than the set of all frequent itemsets.
它所包含的模式数量比频繁集所包含的模式数量小若干数量级。
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 。
The iWidget framework provides the ability to manage and persist name-value pairs called itemSets.
iWidget框架提供管理和持久化称为ItemSet 的名-值对的能力。
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。
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。
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.
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。
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。
The algorithm is based on row enumeration, which can find high-utility long itemsets directly by intersecting long transactions, without extending short itemsets step by step.
该算法基于行枚举，通过长事务的交集运算，直接得到长项集，不必从短项集逐步扩展得到长项集。
The process of discovering frequent itemsets is a problem of the high cost, and how do we complete the updated frequent itemsets under a new minimum support is obviously important.
如何实现不同最小支持度下频繁项目集的更新就显得尤为重要。
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。
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。
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