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- 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。
- weighted maximal frequent subgraph 最大加权频繁子图
- maximal frequent subgraph 最大频繁子图
- Frequent subgraph mining is an active research topic in the data mining community. 摘要 如何从大量的图中挖掘出令人感兴趣的子图模式已经成为数据挖掘领域研究的热点之一。
- Another problem is previous frequent subgraph mining algorithms treat graphs uniformly while graphs have different importance actually. 传统的频繁子图挖掘方法对满足最小支持度阈值的子图同等对待,但在真实数据库中不同的子图往往具有不同的重要程度。
- 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。
- 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。
- Survey of Frequent Subgraph Mining 频繁子图挖掘算法综述
- frequent subgraph mining algorithm 频繁子图挖掘算法
- Maximal Frequent Path (MFP) method 最大频繁访问路径方法
- maximal frequent item sequence sets 最大频繁项目序列集
- maximal frequent item set mining 最大频繁项集挖掘
- Based on the depth-first searching method, all the frequent subgraphs could he searched by adding edges progressively. 在此基础上,算法利用深度优先的思想,通过逐步扩展频繁边找出所有频繁子图。
- 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。
- maximal frequent patterns itemsets 最大频繁集
- Traditional algorithms for frequent subgraphs mining have limits when dealing with biological datasets. 摘要 传统的图挖掘算法应用到生物数据上有其局限性。
- maximal frequent sequential pattern 最大频繁序列模式
- maximal frequent closed itemsets 最大频繁闭项目集
- She will be a maximal help to here. 她到这儿将是个极大的帮助。