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- This paper presents ESEquivPS extension support equivalency pruning strategy, a new search space pruning strategy for mining maximal frequent itemsets to effectively reduce the search space. 为了有效地削减搜索空间,提出了一种新的最大频繁项集挖掘中的搜索空间剪枝策略。
- The characteristics of effective access sequence in the actual application are analyzed and an efficient algorithm OUS based bottom-up strategy is proposed for mining maximal frequent itemsets. 分析实际应用中有效访问序列的特点,提出了一种采用自底向上策略快速挖掘最大频繁项集的OUS算法。
- A Depth-First Search Algorithm for Mining Maximal Frequent Itemsets 一种挖掘最大频繁项集的深度优先算法
- maximal frequent itemsets 最大频繁项集
- maximal frequent itemset 最大频繁项集
- 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。
- maximal frequent patterns itemsets 最大频繁集
- maximal frequent closed itemsets 最大频繁闭项目集
- 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。
- 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。
- By utilizing the byte characteristic, DFMfi can optimize the mapping and unifying operations on the item sets. Moreover, for the first time a method based on bitmap which uses local maximal frequent item sets for fast superset checking is employed. 算法DFMfi充分利用位图的字节特性，优化了项集的匹配和合并操作，并首次在其中引入了基于局部最大频繁项集的超集存在判断方法。
- 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；
- Secondly，weighted maximal frequent subgraph is defined，which can not only discover important maximal subgraph，but also inherit the property of anti-monotony.Thus，the speed of pruning is quickened. 其次，给出了加权最大频繁子图的定义，不仅可以找出较为重要的最大频繁子图，而且可以使挖掘结果同样具有反单调性，从而可加速剪枝。
