挖掘最大频繁模式是多种数据挖掘应用中的关键问题。
Mining maximum frequent patterns is a key problem in data mining research.
挖掘和更新最大频繁模式是多种数据挖掘应用中的关键问题。
Mining and updating maximum frequent patterns is a key problem in data mining research.
同时，此算法还能以较少的空间代价快速查找最大频繁模式。
At the same time, this algorithm is fast in searching for maximum frequent patterns with less space.
因此，文章提出了一种最大频繁模式的快速挖掘算法DMFP及更新算法IUMFP。
This paper proposes a fast algorithm DMFP and an updating algorithm IUMFP, which are based on Prefix Tree for mining maximum frequent patterns.
理论分析和试验结果表明该算法是可行的，并且具有计算性能线性于最大频繁模式总和的优点。
The theoretical analysis and experimental results show that this algorithm scales linearly in the total size of maximal tree pattern and works efficiently in practice.
本文以标记有序树作为半结构化数据的数据模型，研究了半结构化数据的树状最大频繁模式挖掘问题。
In this paper, labeled ordered tree is used as the data model of semi structured data, the problem of maximum tree structured frequent pattern mining from semi structured data is studied.
给出基于约束的频繁最大模式的定义和挖掘基于约束的频繁最大模式算法。
It gives the definition of the frequent maximum pattern with constraint and develop an algorithm for mining frequent maximum patterns with convertible anti-monotone constraint.
在模式挖掘方面，集成了目前有效的最大向前路径挖掘算法和频繁遍历路径挖掘算法，并且将孤立点分析方法引入日志挖掘中。
Efficient mining algorithm of maximum forward path and efficient mining algorithm of frequent traversal path are integrated in the mining period; Outlier analysis is introduced into the mining system.
在模式挖掘方面，集成了目前有效的最大向前路径挖掘算法和频繁遍历路径挖掘算法，并且将孤立点分析方法引入日志挖掘中。
Efficient mining algorithm of maximum forward path and efficient mining algorithm of frequent traversal path are integrated in the mining period; Outlier analysis is introduced into the mining system.
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