频繁模式挖掘是多种数据挖掘应用中的关键问题。
Frequent pattern mining is a key problem in many data mining application.
数据流频繁模式挖掘是数据流挖掘的基础研究之一。
Frequent pattern mining is one basic research of data stream mining.
重点介绍了频繁模式技术在电信收入保障中的应用。
Frequent pattern technology in application of telecommunications income security is highlighted.
频繁模式挖掘的研究对象包括事务、序列、树和图。
Frequent patterns mining involves mining transactions, sequences, trees and graphs.
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.
在频繁模式挖掘过程中能够动态改变约束的算法比较少。
A new algorithm, constrain-based frequent patterns mining, was developed to provide frequent pattern mining with constraints.
为了解决这一问题，提出了一种多关系频繁模式挖掘算法。
In order to solve this problem, this paper proposed a multi-relational frequent pattern mining algorithm.
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.
国内外学者已提出许多新的挖掘数据流频繁模式的方法和技术。
Many new techniques and methods on frequent pattern mining in data stream have been proposed.
上述工作可以为频繁模式挖掘及关联规则的研究提供有益的参考。
The above work can give a valuable reference for frequent pattern mining and association rules studying.
基于序贯频繁模式挖掘，提出并实现了一种宏观网络流量异常检测的方法。
This paper presents and implements a macro-network traffic anomaly detection strategy based on sequential frequent pattern mining.
挖掘这种规则时，可以忽略支持度阈值，因此可同时得到频繁模式和非频繁模式。
When mining such rules, the support threshold can be ignored, so the frequent and infrequent patterns can be produced together.
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.
采用关联规则挖掘算法对图像纹理的频繁模式进行挖掘，通过联合关联规则来表达纹理。
The frequent patterns of texture could be mined by the algorithm of association rules mining. The association rules could be combined to represent the texture.
频繁模式挖掘是数据挖掘领域的一个重要方面，研究内容一般包括事务、序列、树和图。
Frequent patterns mining is an important aspect of data mining and includes mining transaction, sequence, tree and graph.
在此基础上，进一步介绍了频繁模式挖掘的经典算法以及电信收入保障系统的体系结构。
On this basis, further information on the classic frequent pattern mining algorithm and income security systems of telecommunications architecture is introduced.
挖掘事务数据库、时间序列数据库中的频繁模式已经成为数据挖掘中很受关注的研究方向。
Mining frequent patterns in transaction databases, time series databases, and many other kinds of databases has been studied popularly in data mining research.
频繁模式挖掘是最基本的数据挖掘问题，由于内在复杂性，提高挖掘算法性能一直是个难题。
Frequent pattern mining is a fundamental data mining problem for which algorithms still suffer from inefficiencies because of the inherent complexities.
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.
和已有的算法相比，它采用了新的频繁模式搜索策略，大幅度减少了在构造中间数据方面的工作量。
It differs from the previous algorithms in applying a new frequent-pattern searching strategy, which greatly reduces the workload of building intermediate data.
提出了频繁模式的新颖性概念和基于客观度量与主观度量的综合评价方法，强化了对关联规则的评价。
Presents concept of frequent model and comprehensive evaluation method combined the objective and subjective measurements, which enhances the evaluation about association rules.
分析结果表明，利用规则兴趣度能够大大减小候选项目集的大小，有效提高频繁模式挖掘算法的效率。
The result indicates that we can remarkably decrease the candidate items and improve the efficiency of mining frequent pattern when using the interest measure.
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.
提出了一种可直接用于快速频繁模式挖掘的频繁项目表的概念，并实现了具体的频繁模式增量挖掘方法。
Based on a new idea of frequent item table which can be directly used in fast frequent mode mining, an effective FP_growth mining algorithm is presented in this paper.
当数据库中的项目数目较大且事务数量巨大时，频繁模式增长算法内存开销很大，可能导致内存空间不足的现象。
When there are a great many of items and transactions in the database, frequent-pattern growth algorithm needs more additional computer memory, which may cause the lack of memory.
该方法主要是针对频繁模式的挖掘，现有的频繁模式挖掘的模型各有优点，但在智能性和通用性方面表现的较差。
The technique focus on frequent pattern mining, the existed models of frequent pattern mining have the advances respectively, but there is shortcoming in the intelligence and universality.
在论述数据流管理系统模型的基础上，深入分析了国内外的各种频繁模式挖掘算法，并指出这些算法的特点及其局限性。
Based on comment of DSMS model, various frequent pattern mining algorithms are analyzed thoroughly and their characteristics and limitation are pointed out in this paper.
除此之外，算法对频繁模式树中每个节点增加一个位串来存储该项目的前缀项目，以避免在模式扩展的时候频繁的遍历子树。
In addition, we add a bit string to each node in the tree to store the prefix of the items. By using the structure we can avoid repeatedly traversing the sub-tree while expanding the patterns.
除此之外，算法对频繁模式树中每个节点增加一个位串来存储该项目的前缀项目，以避免在模式扩展的时候频繁的遍历子树。
In addition, we add a bit string to each node in the tree to store the prefix of the items. By using the structure we can avoid repeatedly traversing the sub-tree while expanding the patterns.
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