The sequence analysis is a two-stage process for sequential pattern mining 1.
顺序分析是一个两阶段的顺序模式挖掘流程1。
The research of sequential pattern mining techniques is very importantly meaningful.
因此，序列模式挖掘技术研究具有重要的实际意义。
At present streaming data mining is the main mode of mining sequence pattern mining.
目前流数据挖掘的主要挖掘模式是序列模式挖掘。
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。
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。
Experiments show great performance gains over existing sequential pattern mining algorithms, especially for large database.
实验证明，该算法在大规模数据的处理上比现有序列模式挖掘算法有更好的性能。
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。
The corresponding prefetching algorithm, which comes from the sequential pattern mining method in data mining area, is also raised.
借助数据挖掘领域中序列模式挖掘的方法，提出了相应的预取算法。
This paper analyses all kinds of algorithms used on sequential pattern mining and discusses traditional similarity search techniques.
本文主要研究了时间序列数据挖掘方法中的序列模式和相似性搜索。
The third is finding the information of products use and special rules by using the sequence pattern mining in the Data mining technique.
其三是使用数据挖掘技术中的序列模式挖掘技术获得产品使用情况和特殊规律的信息。
As an effective means to analyze timed data sequential pattern mining can extract episode rules from alarms, which is helpful to analyze correlation.
序列模式挖掘作为一种时序数据分析的有效手段，能够自动从告警中提取出有助于关联分析的情景规则。
For the pattern mining, as users do not know which kinds of patterns in data are interesting, we need to detect different patterns as many as possible.
对于模式挖掘，由于用户不知道数据中什么类型的模式是有趣的，所以需要尽可能的挖掘多种不同的模式。
This paper firstly introduces the basic concept of sequential pattern mining, then describes the main algorithms and finally analyzes their performance.
先介绍序列模式挖掘中的基本概念，然后描述几个重要算法，最后给出性能分析。
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。
Based on some sequential pattern mining algorithm, the dissertation analyzed the shortage of the MEMISP algorithm, and proposed an improved MEMISP algorithm.
本文在研究当前比较流行的一些序列模式挖掘算法的基础上，重点分析了MEMISP算法的不足。
In recent years, for the search technology many scholars have designed more efficient sequential pattern mining algorithm which more meet the needs of users.
近年来很多学者针对搜索技术提出了效率较高，符合用户需求的序列模式挖掘算法。
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。
Recently the study on data mining of time series mainly concentrates on both the similarity search in a time series database and the pattern mining from a time series.
时间序列存在于社会的各个领域，对于时间序列数据挖掘的研究目前主要集中在相似性搜索和模式挖掘上。
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。
An improved sequential pattern mining algorithm is proposed, which is based on sliding time window and can discover general earthquake sequences according to field knowledge.
结合地震预报的领域知识，面向具体的应用，提出了一种改进的基于滑动时间窗口的序贯模式挖掘算法，用来发现广义的地震序列。
Data after simply integer mapping can be used as the input of sequential pattern mining, this afford high quality data and uniform format for quickly mining sequential pattern.
数据仓库中的数据经过简单的整数映射可以直接作为序列模式挖掘算法的输入，为高效地挖掘序列模式提供了高质量的数据和统一的格式。
Because the HL7 standard has just proposed and is also in the process of further consummation, electronic medical record data extract and pattern mining have been studied little.
由于HL7标准提出不久，且还处于进一步完善的过程中，电子病历数据抽取和模式挖掘在国内外都少有研究。
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。
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。
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