该连接器能够以并行或序列模式处理分区数据库。
The connector can work with partitioned databases in parallel or sequential mode.
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.
文中还给出了序列模式图的有关性质及构造算法。
The properties and construction algorithm of SPG are presented also.
概念格适用于挖掘包括序列模式在内的各种知识。
The Concept Lattice is suitable to discover various knowledge including sequential patterns.
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.
实验结果显示计算频繁序列模式精简基是很有前途的。
Experimental results showed that computation of condensed frequent sequential pattern base is promising.
因此，研究如何有效地挖掘模糊序列模式变得尤为重要。
Therefore it gets very important to study how we can mine the fuzzy sequential patterns efficiently.
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.
但是，与teradata连接器不同，它只在序列模式下运行。
However, unlike the Teradata connector, it only runs in sequential mode.
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.
在挖掘过程中，本文提出了一种新的挖掘松散的间断序列模式的算法。
In the mining process, we present a new algorithm of loose break sequence mining.
在序列模式的增量式挖掘算法中，IUS算法是目前最为先进的算法。
Of all the incremental mining algorithms, the IUS is the most advanced at present.
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.
时间序列模式、分类规则和关联规则挖掘是当前数据挖掘研究中一个热点。
It is a hotspot that the data mining of time serial model, classify rule, association rule in the data mining study currently.
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.
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。
Association rule and sequential pattern techniques are the methods that study and find the relationship in the items of transaction database.
通过对数据挖掘中的序列模式发现的研究，提出一种新的序列模式发现算法。
By discovery pattern sequences of functions and recognize them, the differences and flaws of the UnifiedKernel system could be found.
根据特征模式复杂性，可分为频繁项模式、频繁序列模式以及频繁子树模式等。
According to the complexity of pattern, the mined characteristic patterns could be sorted as frequent item, frequent sequence and frequent sub tree, etc.
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.
该算法构造了前缀树表示序列模式，并用广度剪枝和深度剪枝维护该前缀树的结构。
It constructs a prefix tree to represent the sequence patterns, and continuously maintains the tree structure by using width pruning and depth pruning.
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.
此作业只能在DataStage指导者节点上以序列模式运行，并且它适合于小量数据提取。
This job can run only in sequential mode on the DataStage conductor node and it is suited for small data extraction.
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.
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.
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.
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.
已发现的序列模式代表了数据库序列中最一般的子序列，因此可用它来确定数据序列的相似性。
The discovered sequential patterns represent the most common subsequences with the database sequences and can be used to determine their similarity.
已发现的序列模式代表了数据库序列中最一般的子序列，因此可用它来确定数据序列的相似性。
The discovered sequential patterns represent the most common subsequences with the database sequences and can be used to determine their similarity.
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