Frequent pattern mining is one basic research of data stream mining.
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 a key problem in many data mining application.
频繁模式挖掘是多种数据挖掘应用中的关键问题。
Many new techniques and methods on frequent pattern mining in data stream have been proposed.
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。
The above work can give a valuable reference for frequent pattern mining and association rules studying.
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。
In order to solve this problem, this paper proposed a multi-relational frequent pattern mining algorithm.
为了解决这一问题，提出了一种多关系频繁模式挖掘算法。
A new algorithm, constrain-based frequent patterns mining, was developed to provide frequent pattern mining with constraints.
在频繁模式挖掘过程中能够动态改变约束的算法比较少。
This paper presents and implements a macro-network traffic anomaly detection strategy based on sequential frequent pattern mining.
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。
Frequent pattern mining is a fundamental data mining problem for which algorithms still suffer from inefficiencies because of the inherent complexities.
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。
On this basis, further information on the classic frequent pattern mining algorithm and income security systems of telecommunications architecture is introduced.
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。
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.
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。
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.
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。
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.
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。
After analyzing and summarizing on ideas, key technology, advantage and disadvantage for some representative frequent pattern mining algorithms have been done, the typical application is listed.
首先对有代表性的挖掘算法从算法思想、关键技术、算法的优缺点进行了分析概括，此后列举了一些典型频繁模式及关联规则的领域应用。
How to generate candidate frequent sequential pattern and calculate its support is a key problem in mining frequent sequential patterns.
如何确定候选频繁序列模式以及如何计算它们的支持数是序列模式挖掘中的两个关键问题。
Sequential pattern mining, which discovers frequent subsequences as interesting patterns in a sequence database.
序列模式挖掘就是发现序列数据库中的频繁子序列作为用户感兴趣的模式。
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.
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。
The result indicates that we can remarkably decrease the candidate items and improve the efficiency of mining frequent pattern when using the interest measure.
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。
Mining Time Series Frequent Sub-pattern based on Pattern Representation can enormously increase the efficiency and veracity of mining, and get twice the result with half the effort.
在时间序列的模式表示的基础上挖掘其频繁子模式，可以大大提高挖掘的效率和准确性，达到事半功倍的效果。在该算法中，还使用了一定的剪枝策略，使得算法的时间复杂度进一步降低。
Mining Time Series Frequent Sub-pattern based on Pattern Representation can enormously increase the efficiency and veracity of mining, and get twice the result with half the effort.
在时间序列的模式表示的基础上挖掘其频繁子模式，可以大大提高挖掘的效率和准确性，达到事半功倍的效果。在该算法中，还使用了一定的剪枝策略，使得算法的时间复杂度进一步降低。
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