Applications of alarm sequential pattern mining are studied in this paper.
论文研究了序列模式挖掘在网络告警分析中的具体应用。
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。
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。
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。
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。
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。
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。
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。
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