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- frequent closed item set 频繁闭项集
- An Efficient Algorithm for Mining Association Rules Based on Frequent Closed Item Sets 基于频繁闭项目集的关联规则挖掘算法
- frequent closed item 闭合频繁项集
- The Personalize item settings dialogue box appears. 一个私人设置的对话框出现了。
- For reducing the spaces of rule database and facilitating users to query,the minimal prediction set is used and mined using maximum frequent item sets which are found by a set-enumeration tree. 为缩减关联规则存储空间和方便查询关联规则,提出一种前件为单一项目的最小预测集算法。
- 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
- 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。
- The method generates an item set of low dimensionality and a set of classification rules after dealing with the DSM. 通过对差异-相似矩阵的处理，最终得到维度较低的文本特征集，并同时生成分类规则。
- Therefore, our item set scoops out to search the language to standardize to scoop out the function with data in the data heavy did with the aspect beneficial quest. 因此,可以说我们项目组在数据挖掘查询语言标准化和数据挖掘功能的可重用方面做出了有益的探索。
- Text: Soong Qingling Children Education Prize is a new item set up by China Welfare Institute and Shanghai S.C.L.Foundation. "宋庆龄幼儿教育奖"是中国福利会与上海宋庆龄基金会今年刚刚设立的一个奖项。
- Text: Soong Qingling Children Education Prize is a new item set up by China Welfare Institute and Shanghai S.C.L.Foundation.Ms. "宋庆龄幼儿教育奖"是中国福利会与上海宋庆龄基金会今年刚刚设立的一个奖项。
- This algorithm can generate new candidate item sets effectively using the frequent item sets in the knowledge database, so it can avoid the problem that candidate item sets is very large. 该算法可以有效利用知识数据库中保留的最小非高频项目集来产生新的候选项目集,避免了候选项目集的数量太庞大的问题。
- 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。
- maximal frequent closed itemsets 最大频繁闭项目集
- minimum frequent closed itemsets 最小频繁闭项目集
- minimum strong frequent closed itemsets 最小强频繁闭项目集
- Based on the traditional Apriori and DHP, this algorithm not only puts forward some new concepts such as the equivalent item set and check point, but also improves the Apriori gen in Apriori algorithm. 该算法以经典的 Apriori和 DHP算法为基础 ;提出了中间检查点、等价项目类等概念 ;并对 Apriori中的 Apriori- gen算法进行了改进 .
- Item Sets, in this case, are products that are purchased together that meet the minimum support and confidence. 条目集,在这种情况下是一起购买的符合最低支持度和可信度的产品。
- To use the difference command, you must have the Read permission for all specified items set to Allow. 若要使用difference命令,您必须将对所有指定项的“读”权限设置为“允许”。
