顺序分析是一个两阶段的顺序模式挖掘流程1。
The sequence analysis is a two-stage process for sequential pattern mining 1.
频繁模式挖掘是多种数据挖掘应用中的关键问题。
Frequent pattern mining is a key problem in many data mining application.
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.
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 one basic research of data stream mining.
频繁模式挖掘的研究对象包括事务、序列、树和图。
Frequent patterns mining involves mining transactions, sequences, trees and graphs.
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.
在频繁模式挖掘过程中能够动态改变约束的算法比较少。
A new algorithm, constrain-based frequent patterns mining, was developed to provide frequent pattern mining with constraints.
为了解决这一问题，提出了一种多关系频繁模式挖掘算法。
In order to solve this problem, this paper proposed a multi-relational frequent pattern mining algorithm.
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.
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。
The above work can give a valuable reference for frequent pattern mining and association rules studying.
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.
目前对于时间序列数据挖掘的研究主要集中在相似性搜索和模式挖掘上。
Opposite to mature part of data mining (such as mining of database association rules and classify rules), mining of time series still falls into a new branch.
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。
This paper presents and implements a macro-network traffic anomaly detection strategy based on sequential frequent pattern mining.
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.
经销店期望通过寻找数据内的模式挖掘这些数据并使用群集来判断其客户是否有某种行为特点。
They are hoping to mine this data by finding patterns in the data and by using clusters to determine if certain behaviors in their customers emerge.
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.
这作为全文研究的基础，贯穿于时间序列部分周期模式挖掘和增量挖掘分析的全过程。
They are the foundation of research on Algorithm of Mining Partial Periodic Patterns and they can be used in the whole paper.
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.
频繁模式挖掘是数据挖掘领域的一个重要方面，研究内容一般包括事务、序列、树和图。
Frequent patterns mining is an important aspect of data mining and includes mining transaction, sequence, tree and graph.
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.
提出了一种可直接用于快速频繁模式挖掘的频繁项目表的概念，并实现了具体的频繁模式增量挖掘方法。
Based on a new idea of frequent item table which can be directly used in fast frequent mode mining, an effective FP_growth mining algorithm is presented in this paper.
提出了一种可直接用于快速频繁模式挖掘的频繁项目表的概念，并实现了具体的频繁模式增量挖掘方法。
Based on a new idea of frequent item table which can be directly used in fast frequent mode mining, an effective FP_growth mining algorithm is presented in this paper.
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