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。
Experimental results showed that computation of condensed frequent sequential pattern base is promising.
实验结果显示计算频繁序列模式精简基是很有前途的。
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。
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。
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。
Association rule and sequential pattern techniques are the methods that study and find the relationship in the items of transaction database.
关联规则和序列模式是研究和发现事务数据库中数据项之间的相关性的方法。
The access patterns to system memory and graphics local memory are averaged because there is no time-based sequential pattern to make them exact.
对系统存储器和图形本地存储器的访问模式是平均的，因为没有使它们准确的基于时间的顺序模式。
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。
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。
The time trait is often ignored in the course of mining traditional sequential pattern, in which the sequential item is also without attribute constraint.
传统序列模式挖掘算法往往忽略了序列模式本身的时间特性，所考查的序列项都是单一事件，无属性约束。
The sequential pattern is a very useful knowledge one in time related databases, and it is very important to find out high efficient algorithm of mining it.
序贯模式是时间相关数据库中存在的一种十分有用的知识模式，其发掘方法的研究有着十分重要的意义。
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。
Also, some of data mining algorithms that are commonly used in Web Usage mining are clustering, association rule generation, sequential pattern generation etc.
同时本篇论文也主要提出了一些经常被使用的数据挖掘的算法像聚类挖掘、关联规则挖掘、序列模式挖掘等。
If sequential pattern of grain reduction occurs, the density growth only gives information about the distribution of development induction period among grains.
顺序还原时，乳剂层的密度增长速率只是给出了颗粒间显影诱导期分布的信息。
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。
Compared to traditional research on earthquake sequence in seismology, data mining is applied to earthquake prediction, and sequential pattern is used to earthquake sequences.
与地震学中地震序列研究相比，将数据挖掘的应用拓展到地震预报中，通过序贯模式来研究广义地震序列。
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。
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。
In pattern generation for sequential circuits at RTL, simulation-based methods avoid the large search space used in time-frame expansion methods, but the quality of patterns cant be guaranteed often.
在时序电路的RTL激励生成中，基于模拟的方法避免了帧扩展法庞大的搜索空间，但采用该方法常存在向量过多，质量不高等问题。
The 3-D pattern on sequential stratigraphy proposed by Vail et. al (1988, 1989) can be considered as a basis on which their new idea of sequential stratigraphy is established.
维尔等人（1988，1989）提出的层序地层立体模式，可以说是他们的层序地层学新概念立论的基础，也是他们赖于进行地层和岩相解释的依据。
The 3-D pattern on sequential stratigraphy proposed by Vail et. al (1988, 1989) can be considered as a basis on which their new idea of sequential stratigraphy is established.
维尔等人（1988，1989）提出的层序地层立体模式，可以说是他们的层序地层学新概念立论的基础，也是他们赖于进行地层和岩相解释的依据。
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