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- Experimental results showed that GGTWFPMiner was an effective weighted frequent patterns mining algorithm based on graph traversals. 实验结果表明，该算法是一个高效的基于图遍历的加权频繁模式挖掘算法。
- Using Frequent Pattern Mining for Image Labeling 利用频繁模式挖掘进行图像标注
- Temporal Frequent Pattern Mining Algorithm (TemFP) 时态频繁模式挖掘算法（TemFP）
- candidate combination frequent pattern mining 候选组合频繁模式挖掘
- SQL-based Frequent Pattern Mining Without Candidacy Generation 基于SQL的不产生候选集的频繁模式挖掘
- An Alert Correlation and Analysis Algorithm Based on Frequent Pattern Mining 基于频繁模式挖掘的报警关联与分析算法
- frequent patterns mining 频繁模式挖掘
- frequent pattern mining 频繁模式挖掘
- 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。
- Access pattern mining module fulfills the FAP-Mining algorithm. 访问模式挖掘模块实现了本文第四章提出的FAP-Mining算法。
- Based on this model, a new algorithm called Global Graph Traversals-based Weighted Frequent Patterns Miner (GGTWFPMiner) was presented. 基于该模型，提出了基于全局图遍历加权频繁模式挖掘算法。
- Next, after near 10 years research and development, the most essential phase in association rules mining, frequent pattern acquirement, and its techniques have been improved dramatically. 其次，在经历了近10年的发展以后，关联规则挖掘中至关重要的频繁模式获取技术得到了很大的发展。
- Based on the above algorithm, a FCL based sequential patterns mining algorithm (SECLSP) is implemented. 在此基础上,实现了基于频繁概念格的序列模式发现算法SECLSP。
- Using frequent pattern tree,it divides and rules the alarms during the clustering process. 然后举例说明了利用该算法进行聚类的过程。
- Results of the analysis performed on the editorials indicated that the most frequent pattern pertaining to all the studied newspapers was LFV. “一样的花费，更好的质量和服务”，这是新华翻译社对客户的承诺。
- In order to overcome the disadvantage of low efficiency of KM-AOI algorithm,an AOI clustering algorithm based on frequent pattern tree is presented. 为了克服KM-AOI算法聚类效率较低的缺点,提出了基于频繁模式树的AOI聚类算法,即在聚类过程中借助频繁模式树,采取分而治之的策略处理警报集以得到规则。
- Sequential pattern mining, which has broad applications, is one of key research areas in data mining. 摘要序列模式挖掘是数据挖掘中一个重要研究方向，具有广泛的应用背景。
- In addition, FPMFI also compresses the conditional FP-Tree (frequent pattern tree) greatly by deleting the redundant information, which can reduce the cost of accessing the tree. 另外;算法FPMFI通过删除FP子树(conditional frequent pattern tree)的冗余信息;有效地压缩了 FP 子树的规模;减少了遍历的开销.
- Then, this paper proposed an increment update algorithm TW-CFI based on frequent pattern trees and sliding windows.This algorithm adapted the characteristics of data streams. 然后,基于频繁模式树,结合滑动窗口技术提出了一种增量更新算法TW-CFI,它能适应数据流的特点并且可以挖掘数据流频繁闭项集信息。
