目前流数据挖掘的主要挖掘模式是序列模式挖掘。
At present streaming data mining is the main mode of mining sequence pattern mining.
因此，序列模式挖掘技术研究具有重要的实际意义。
The research of sequential pattern mining techniques is very importantly meaningful.
论文研究了序列模式挖掘在网络告警分析中的具体应用。
Applications of alarm sequential pattern mining are studied in this paper.
提高序列模式挖掘算法效率的关键在于减少发现频繁序列的时间。
To speed up mining sequential patterns, reducing the time cost is very important during discovering sequential frequent sequence.
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。
The corresponding prefetching algorithm, which comes from the sequential pattern mining method in data mining area, is also raised.
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。
Sequential pattern mining, which discovers frequent subsequences as interesting patterns in a sequence database.
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。
This paper firstly introduces the basic concept of sequential pattern mining, then describes the main algorithms and finally analyzes their performance.
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。
Experiments show great performance gains over existing sequential pattern mining algorithms, especially for large database.
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。
In recent years, for the search technology many scholars have designed more efficient sequential pattern mining algorithm which more meet the needs of users.
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。
The third is finding the information of products use and special rules by using the sequence pattern mining in the Data mining technique.
以往的序列模式挖掘往往只考虑一些顺序的模式，而将一些重要的非顺序的模式忽略了。
Formerly, Sequential patterns Mining often only calculates some ordinal patterns, but it ignores some out-of-order 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。
How to generate candidate frequent sequential pattern and calculate its support is a key problem in mining frequent sequential patterns.
在移动通信环境中，移动序列模式挖掘对于有效的提高位置管理的服务质量具有重大的意义。
Mining moving sequential patterns has great significance for effective and efficient location management in wireless communication systems.
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。
Based on some sequential pattern mining algorithm, the dissertation analyzed the shortage of the MEMISP algorithm, and proposed an improved MEMISP algorithm.
序列模式挖掘是数据挖掘的重要分支，GSP算法与PSP算法是序列模式挖掘中的两种典型算法。
Mining sequential patterns is an important branch of data mining, and the GSP and PSP are the main two algorithms for mining sequential patterns.
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。
As an effective means to analyze timed data sequential pattern mining can extract episode rules from alarms, which is helpful to analyze correlation.
系统的数据挖掘模块应用了序列模式挖掘中的GSP算法，并对其进行了改进，引入了主属性及兴趣度。
The main attribute and interest measure are introduced to improve the (GSP) algorithm, which is then applied in the data mining module of the system.
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 time trait is often ignored in the course of mining traditional sequential pattern, in which the sequential item is also without attribute constraint.
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。
Also, some of data mining algorithms that are commonly used in Web Usage mining are clustering, association rule generation, sequential pattern generation etc.
其思想是通过将网络审计数据转化为时序数据库，对其进行序列模式挖掘以提炼出用户行为模式，并由此进行异常检测。
The idea is to transform the net audit data into time series database and mine the sequence pattern to extract the user behavior pattern , and then to use behavior pattern in anomaly detection.
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。
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.
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。
Compared to traditional research on earthquake sequence in seismology, data mining is applied to earthquake prediction, and sequential pattern is used to earthquake sequences.
第五章利用时间序列的方法对证券交易数据进行了挖掘，找出了数据中的模式和异常，相对传统方法而言，给出了更精确的预测模型和异常挖掘方法。
In the final chapter, we mine stock trading data using time series method, find out the model and outliers in the data and, at last, we show the more exact forecasting model and outlier mining method.
其次，分析了数据挖掘中所使用的关联规则和序列模式，对关联规则和序列模式的各种挖掘算法进行了比较。
Secondly, it analyzed association rule and sequence mode used in the process of data mining and compared the main algorithms of association rule and sequence mode.
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。
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.
挖掘序列模式是数据挖掘的主要内容之一，目前已有许多序列模式模型和相应的挖掘算法。
Mining sequential patterns is one of the central content in data mining. There have been many models of sequential patterns and algorithms for mining sequential patterns.
数据挖掘领域一个活跃的研究分支就是序列模式的发现，即在序列数据库中找出所有的频繁子序列。
An active research in data mining area is the discovery of sequential patterns, which finds all frequent sub-sequences in a sequence database.
本文对时间序列模式、分类规则和关联规则挖掘的方法进行了深入的研究。
In this thesis, the thorough study of time serial model, classification rule and association rule is made.
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。
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。
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.
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