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- An Algorithm Mining Frequent Item Set and their Related Transaction Set 频繁项目集及相关事务集的挖掘算法
- Research and Realization Using Prolog on Mining Frequent Items Algorithm of Association Rules 挖掘关联规则频繁项集的算法研究及其Prolog实现
- mining frequent items 频繁项集挖掘
- 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 searching space of frequent temporal sequence patterns could be reduced and the efficiency of association rule mining could be improved by projecting frequent items in time windows to prefix projected accumulation trees. 将时间窗口内频繁项的信息映射到前缀映射累加树中，以降低频繁时序模式的搜索空间，提高时序关联规则的挖掘效率。
- This paper also discusses how to set the optimal minimum support for the common association rules mining algorithm,which can guarantee the frequent items are the weighted frequent items' superset. 同时,给出了最优的最小支持度设定方法,保证了普通关联规则算法所产生的频繁集为加权频繁集的超集。
- Thinking about the amount of hits on homepage, this paper improves the algorithm of finding frequent items. 并结合网页特点，考虑到主页的点击率的影响，对生成频繁访问浏览页的算法做了改进；
- The results show that the algorithm will find a passel of frequent items within a few generations. 实际计算结果表明，该方法一般在几代内即可找到一批长频繁模式。
- An algorithm for mining frequent subgraphs based on associated matrix was proposed. 摘要提出了一种基于关联矩阵的频繁子图挖掘算法。
- This paper proposes a new algorithm for mining frequent subsequence in time series based on symbolic representation. 提出一种新的基于符号化表示的时间序列频繁子序列的挖掘算法。
- The limitlessness and mobility of data streams made the traditional frequent item algorithm difficult to apply to data streams. 摘要数据流的无限性和流动性使得传统的频繁项挖掘算法难以适用。
- At last, our experimental result shows that the algorithm FIMA is more effectively than the algorithm DLG based on graph for mining frequent patterns. 试验结果表明该算法比同样基于逻辑与运算的DLG算法挖掘频繁项集的效率更高。
- It proposed an algorithm for mining frequent patterns by finding the frequent extensions and merging sub-trees in a conversely constructed FP-tree. 摘要提出了一种称为逆向FP-合并的算法，该算法逆向构造FP-树并通过在其中寻找频繁扩展项集与合并子树来挖掘频繁模式。
- This paper presents an efficient pattern growth algorithm for mining frequent embedded subtrees in a forest composed of unordered trees. 该文提出用模式增长方法在无序树构成的森林中挖掘嵌入频繁子树。
- 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）。
- maximal frequent item set mining 最大频繁项集挖掘
- constrained frequent item sets mining 约束频繁项挖掘
- FSP adopts the depth-first search strategy to mine frequent connected subgraphs efficiently,without candidate generation. 基于这种字典顺序,FSP算法不需要生成候选,采用深度优先搜索策略挖掘频繁连通子图。
- Experiments show that this algorithm can decrease the number of generated frequent itemsets largely, and reduce the time consumption of mining frequent itemsets evidently. 实验表明，该算法可以大幅度地减少所产生的频繁项集数量，显著地降低了挖掘频繁项集的时间开销。
- Another is that sorting frequent item of not fuzzy attributes in descending order of their support firstly,then sorting database fuzzy attributes with frequent item in ascending order of their nodes number in FFP tree. 先对非模糊属性下的频繁项目按支持度从大到小进行排序,再对模糊属性按其在FFP-树中包含的不同结点的个数,从少到多进行排序,然后依次将各属性下的频繁项目插入到头表中。
