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。
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。
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。
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。
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。
Frequent pattern technology in application of telecommunications income security is highlighted.
重点介绍了频繁模式技术在电信收入保障中的应用。
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。
It differs from the previous algorithms in applying a new frequent-pattern searching strategy, which greatly reduces the workload of building intermediate data.
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。
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.
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.
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。
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