A fault alarms correlation rule analysis model based on sequence clustering algorithm is designed.
设计了基于序列聚类算法的故障告警关联规则分析模型。
So consider embedding less free space when using SSDs, since the clustering sequence may not be so important.
因此考虑在使用SSD 时嵌入较少的空闲空间，因为集群顺序可能并不那么重要。
The main purpose of allocating free space is to keep the data rows in the same physical sequence as the clustering index, thus reducing the need to frequently reorganize the data.
分配空余空间的主要目的是使数据行的物理顺序与群集索引一致，以减少频繁重组数据的需要。
The degree of clustering of an index; that is, the extent to which the physical sequence of rows in a table follows an index.
一个索引的群集程度；即，表中行的物理顺序与索引的符合程度。
DB2 does its level best to keep rows close to their optimal location in the clustering sequence.
DB 2尽最大努力使行接近集群顺序中的最佳位置。
In this paper, a color clustering method is introduced to reduce colors and get effective color representation of image sequence.
本文介绍了一种颜色聚类的方法，在序列图象中进行颜色的量化，以获得有效的图象颜色的表征。
Since the semantic sequence is only related to text, it is available for incremental clustering.
由于所提算法的语义序列只与文本自身相关，所以它适用于增量式聚类。
In this paper, with the aid of the two concepts of diversity between two samples and diversity sequence of ordered samples, diversity sequence method is presented for clustering ordered samples.
本文借助于两个样品之间的差异度和有序样品的差异序列两个概念，提出了有序样品聚类的差异序列法法。
A weighted method of customer's time series is proposed and statistical features of time series are adopted for customer clustering, which make each group of customers have similar sequence feature.
提出了客户时间序列的加权处理方法，并应用客户时间序列的统计特征作为聚类特征向量，采用混合式遗传算法对客户聚类，使每一类客户具有相似的时序特征。
Thereby a sequence of events clustering algorithm, shorted for SOEC, is proposed.
基于此编辑距离，提出一种事件序列聚类算法SOEC。
After the training, characters are extracted from the2 0 unclassified artificial sequence samples and1 82 natural sequence samples to form the character vectors as input of the two NN for clustering.
通过训练后，将20个未分类的人工序列样本和1 82个自然序列样本提取特征形成特征向量并输入两个网络进行分类。
Partitioning method is a clustering algorithm, which is sensible to initial partitions (values of k), initial values and input sequence.
划分方法的缺点是要求事先给定聚类结果数，对初始划分和输入顺序敏感等。
CD-HIT is a widely used program for clustering and comparing large biological sequence datasets.
CD -HIT是用来聚类和比较大的生物学序列数据集的一个广泛使用的程序。
The clustering strategy can efficiently identify gene family and alternate splicing forms of expressed sequences. It can also reduce the adverse effects caused by sequence errors.
这一聚类策略能降低测序错误带来的影响，有效识别基因家族成员，并避免选择性剪接的干扰。
After the training, characters are extracted from the 20 unclassified artificial sequence samples and 182 natural sequence samples to form the character vectors as input of the two NN for clustering.
通过训练后，将20个未分类的人工序列样本和182个自然序列样本提取特征向量并输入两个网络进行分类。
After the training, characters are extracted from the 20 unclassified artificial sequence samples and 182 natural sequence samples to form the character vectors as input of the two NN for clustering.
通过训练后，将20个未分类的人工序列样本和182个自然序列样本提取特征向量并输入两个网络进行分类。
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