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- global frequent itemsets 全局频繁项目集
- global frequent itemset 全局频繁项目集
- The problem of fuzzy constraint in frequent itemset mining is studied. 摘要研究频繁项集挖掘中的模糊约束问题。
- The famous algorithms to find the frequent itemsets include Apriori and FP-growth. 经典的生成频繁项目集集合的算法包括Apriori算法和FP-growth算法。
- Discovering frequent itemsets is a key problem in data mining association rules. 发现频繁项目集是关联规则数据开采中的关键问题。
- Discovering maximum frequent itemsets is a key problem in many data mining applications. 发现最大频繁项目集是多种数据开采应用中的关键问题 .
- The HCM sketch uses breadth-first search strategy to identify and evaluate the hierarchical frequent itemsets. 基于该结构,利用广度优先查询策略,查找多层频繁项集和估计多层频繁项值。
- In data mining,IUA algorithm has a problem that the frequent itemsets are not minined completely. 在关联规则挖掘中,对所挖掘出的关联规则的管理和维护非常重要。
- Fast Mining of Global Maximum Frequent Itemsets 快速挖掘全局最大频繁项目集
- The relation between the suppport and the number of frequent itemsets is analyzed and a comparison between the algorithms FIU and FUP is made. 分析了支持率和频繁模式集合大小的关系，并对算法FIU和算法FUP进行了比较。
- In this paper，a new kind of algorithms BFI-DMFI（Mining Maximum Frequent Itemsets） and BFI-DCFI（Mining Frequent Closed Itemsets） is proposed. 摘要 提出了基于频繁项集的最大频繁项集（BFI-DMFI）和频繁闭项集挖掘算法（BFI-DCFI）。
- Among the proposed algorithms of finding frequent itemsets, DLG is a efficient algorithm to controls I/O cost by reducing the number of database passes. 在已有的频繁集发现算法中 ;DLG算法通过减少事务数据库的扫描次数 ;进而有效减少挖掘过程的I/O代价 .
- It is important that determining the frequent itemsets from a huge amount of candicate itemsets is the most time-consuming part of the process in Apriori algorithm. 随着大量数据不停地收集和存储,许多业界人士对于从他们的数据库中挖掘关联规则越来越感兴趣。
- In BFI-DMFI，we can confirm whether a frequent itemsets is also a maximum frequent itemsets through detecting whether exiting their superset itemsets in frequent itemsets. BFI-DMFI算法通过逐个检测频繁项集在其集合中是否存在超集确定该项集是不是最大频繁项集；
- The MFIA_VTL algorithm finds maximum frequent itemsets through partitioning itemsets search space based on the prefix in the database with the vertical tid-list of transactions. 该算法针对数据库的垂直事务标识列表结构对项集搜索空间进行基于前缀的划分，来发现最大频繁项集。
- Grahne G,Zhu Jian-fei.Fast algorithms for frequent itemset mining using FP-Trees[J].IEEE Transactions on Knowledge and Data Engineering,2005,10:1347. 胥桂仙;高旭;于绍娜;关联规则算法在中文文本挖掘中的应用研究[J].;中央民族大学学报;2004;13(4):332-338
- In BFI-DCFI，in order to generate frequent closed itemsets，we can make use of mining maximum frequent itemsets from frequent itemsets which have equal support. BFI-DCFI算法则是通过挖掘所有支持度相等的频繁项集中的最大频繁项集组合生成频繁闭项集。
- After the method for mining association rules is analyzed,a dynamic algorithm of frequent itemsets mining based on undirected itemsets graph is put forward. 通过对关联规则挖掘算法的详细分析,提出了一种基于无向项集图的动态频繁项集挖掘算法。
- However, neither can it ensure items in the antecedent or the consequent of a rule are positively correlated;nor can it reduce the time of mining frequent itemsets. 但是，这并不能保证规则前、后件中的项是正相关的，也不能减少挖掘频繁项集的时间开销。
- The algorithm can get the new frequent itemsets through search the undirected itemsets graph once again,when the database and the minimum support are changed. 当事务数据库和最小支持度发生变化时,该算法只需重新遍历一次无向项集图即可得到新的频繁项集。
