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。
This paper analyses all kinds of algorithms used on sequential pattern mining and discusses traditional similarity search techniques.
本文通过时间震级序列数据挖掘方法对地震预报展开了一系列的研究。
Using data mining techniques can be more systematic, in-depth, comprehensive, detailed research on earthquake prediction analysis play a role in promoting.
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.
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.
另外，本文用模糊集理论对时间序列数据挖掘过程中的不确定性进行了处理，提出了一种模糊时序数据挖掘的框架。
Moreover, fuzzy sets theory is adopted in the dissertation to deal with the uncertainty of the mining process and a new fuzzy frame of TSDM is given then.
而在这其中时间序列数据挖掘是面向特殊应用数据挖掘领域中比较复杂的一个分支，主要研究从大量时间序列历史数据中挖掘有价值信息的方法和相关技术。
Among these research fields, time series data mining is a rather complex branch, which is a technique that extracts the most valuable information from large amount of history time series data.
首先，该算法挖掘频繁序列的数据并计算一个邻接格。
First, the algorithm mines the data for frequent sequences and computes an adjacency lattice.
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.
粗糙集理论作为一种处理模糊和不确定性问题的有效工具，对时间序列的数据挖掘是有效的。
Rough set theory, as an effective tool to deal with vagueness and uncertainty, is effective to the time series data mining.
最大频繁序列发现是数据挖掘中的一个重要分支。
Discovering the maximal frequent sequence is an important branch in data mining.
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.
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.
目前时间序列的数据挖掘是数据挖掘的重要研究热点之一。
Research on time series data mining is one of important hot spots of data mining.
针对在数据挖掘中采用二进制转换的方法，定义了二进制序列集的相关概念并为此提供依据。
Aiming at the method of binary system conversion in data mining, this paper defines binary system sequences set and its related concepts.
它可以进一步应用到无冗余关联规则发现、序列分析等许多数据挖掘问题。
It can be used in the discovery of non-redundant association rules, sequence analysis, and many other data mining problems.
生物序列分析是机器学习和数据挖掘技术一个重要的应用领域。
Biological sequence analysis is an important application domain of data mining technology.
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.
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.
时间序列信息系统中的决策问题的关键是有效地挖掘历史数据中包含的时序信息。
Furthermore, the key problem of decision making in time series information system is how to effectively mine the time order information in history data.
挖掘事务数据库、时间序列数据库中的频繁模式已经成为数据挖掘中很受关注的研究方向。
Mining frequent patterns in transaction databases, time series databases, and many other kinds of databases has been studied popularly in data mining research.
该文针对网管告警数据库中时间序列存在的连续性、不确定性和模糊性问题，提出了一种基于时态关联规则挖掘告警库的新方法。
For the problems of continuity, uncertainty and fuzziness in the time-series of the network management alarm database, this pa-per puts forward a new mining method based on time-series rules.
通过实验验证，在关联规则数据挖掘中采用二进制序列集这一组织数据方法是有效且可行的。
And making an experiment on it, it proves that binary system sequences set is efficient and feasible as an approach of organization data based on mining of association rules.
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.
作为一类重要的复杂类型数据，时间序列已成为数据挖掘领域的热点研究对象之一。
As one of the important forms of complex data, time series is a hotspot in data mining area.
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.
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.
时间序列数据库中相似序列与相似趋势的挖掘，是数据挖掘领域的一个较新的重要问题。
Mining similar sequences and similar trends in time-series databases is a novel and important problem in data mining literature.
从医嘱数据库的药物医嘱序列中挖掘出的知识既可用于评价治疗质量，又可为准确、快速地制定安全有效的药物治疗方案提供必要的依据。
Knowledge mined from sequence of doctors advice for medicine can not only evaluate treatment quality but also provide necessary basis for establishing a safety and effective medicine treatment plan.
从医嘱数据库的药物医嘱序列中挖掘出的知识既可用于评价治疗质量，又可为准确、快速地制定安全有效的药物治疗方案提供必要的依据。
Knowledge mined from sequence of doctors advice for medicine can not only evaluate treatment quality but also provide necessary basis for establishing a safety and effective medicine treatment plan.
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