依据序列模式发现的特点，频繁概念格可以改善模式发现的时空性能。
Considering the characters of the sequential patterns mining, Fequent Concept Lattice can improve the mining efficiency.
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.
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.
实验结果表明该算法能有效发现变长符号序列中的聚类模式。
Simulation results show that our method can successfully find patterns of clusters of the input variable-length sequences.
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.
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.
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.
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.
采用聚类分析方法对交通流时间序列进行分析可以发现典型的交通流变化模式。
By clustering of traffic flow time series, the typical traffic fluctuation patterns can be found.
时间序列相似性模式搜索是营销时间序列数据仓库中知识发现领域的一个研究热点。
The similarity pattern query about time series is one of the research hotspots in knowledge discovering in the time series database.
数据挖掘领域的一个活跃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.
摘要：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。
Absrtact: An active research in data mining area is the discovery of sequential patterns, which finds all frequent sub - sequences in a sequence database.
研究了利用GM(1,1)模型发现时间序列模式的方法，用GM(1,1)模型可以从时间序列中寻找变化规律，预测将来的发展趋势。
Investigates a method of time series model with grey system model. Rules in time series can be found by GM (1, 1) model and the trend of the time series can be forecasted.
提出了一种针对不同时间序列间关联模式的发现方法，并阐述了以该方法为基础而构建的关联模式挖掘系统的结构。
This paper gives an algorithm for mining association patterns between two different time series, and describes the construction of a mining system.
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.
这种状况造成了生物序列比对、模式发现等数据处理的低效率。
This state makes the low efficiency when biological sequence data are processed.
在研究中比较了不同识别分子与靶序列DNA结合的强弱，发现双萘酰亚胺衍生物4对此序列DNA具有高亲合性的结合，并分析了识别分子与DNA复合物的碎裂机理以及结合模式。
It revealed that imide derivative 4, a potent binder of HIV-1 DNA, had the higher binding affinity with the duplex DNA among six DNA-recognizing mole-cules.
在研究中比较了不同识别分子与靶序列DNA结合的强弱，发现双萘酰亚胺衍生物4对此序列DNA具有高亲合性的结合，并分析了识别分子与DNA复合物的碎裂机理以及结合模式。
It revealed that imide derivative 4, a potent binder of HIV-1 DNA, had the higher binding affinity with the duplex DNA among six DNA-recognizing mole-cules.
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