然后，在一个挖掘流中使用这个规则文件，把概念从文本列中提取到关系数据库表中。
We will then use this rule file in a mining flow to extract the concepts from text columns in relational database tables.
能够从关系数据源创建报告是InfoSphere Warehouse数据挖掘与IBMCognos集成的关键。
The ability to create reports from relational datasources is the key to the integration of InfoSphere Warehouse mining and IBM Cognos.
提出了一种基于局部孤立系数(loc)的孤立点挖掘算法。
This paper presents a Local Outlier Coefficient - Based (LOC) Mining of Outliers.
讨论了区间值关系数据库上模糊关联规则的挖掘算法与预测方法。
Mining algorithm and prediction method of fuzzy association rules are discussed in this paper.
该算法是对基于局部稀疏系数(LSC)孤立点挖掘论文中局部稀疏率和局部稀疏系数计算的一种改进。
This algorithm is an improvement of local sparsity ratio and local sparsity coefficient computation for local sparsity coefficient - Based (LSC) Mining of Outliers paper.
附近挖掘的条形基础承载力系数的估计是由使用的运动学极限分析方法在本文中。
An estimate of bearing capacity coefficients for a strip footing near an excavation is made by using the kinematical approach of limit analysis in this paper.
实例分析表明采用新的分辨系数并且结合数据挖掘的关联规则理论的灰色关联评估法是一种有效的方法。
Example shows that a new resolution ratio and combination of data mining association rules theory of gray correlation assessment method is an effective method.
传统的隐私保护关联规则挖掘算法由于没有考虑规则左右件相关系数的影响，对非敏感规则的支持度影响很大。
Traditional privacy preserving association rule mining algorithms do not consider the correlation of the left hand side and the right hand side, which affect non-restrictive rule support negatively.
文章利用统计方法挖掘关系数据库中属性间的统计关系，并讨论了属性间统计关系的应用。
Statistics approaches are used to Mine statistical relationship among attributes in relational databases, meantime this paper discusses applications of statistical relationship.
由于关系数据的竞争聚集算法能得到优化的固定的聚类个数，因此能挖掘出优化的模糊关联规则。
The optimal fuzzy association rules can be mined due to the optimal fixed clustering number that can be obtained by the relational competitive agglomeration algorithm.
与传统的数据挖掘算法相比，多关系数据挖掘特有的复杂性对算法的性能提出了更高的要求。
Compared to the traditional data mining algorithms, the complexity of specific performance of the algorithm in the multi-relational data mining put forward higher requirements.
ZBP不仅充分利用了零树符号之间的相关性，而且从位数据的层面上挖掘出了小波系数值之间的相关性，从而提高了算术编码的性能。
ZBP exploits the correlation among the Zerotree symbols and the bit data of wavelet coefficients, so the efficiency of arithmetic coding is improved.
多关系数据挖掘算法的搜索空间变得更大、更复杂。
The search space of Multi-relational data mining algorithm becomes larger and more complex.
传统数据挖掘算法是面向关系数据库和数据仓库的，不能直接用于XML文档的数据挖掘。
Traditional data mining algorithm is oriented relational database and data warehouse, and can not be directly used for data mining in XML documents.
您可以用关系数据或多维(多维数据集)数据来创建挖掘模型。
You can create Mining Models from relational or multidimensional (cube) data.
因此本文以多关系数据挖掘算法作为研究对象。
That is why this paper chooses a multi-relational data mining algorithm as our research object.
在技术上，数据挖掘是在大型关系数据库几十个领域之间中寻找相互关系或模式的过程。
Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases.
在技术上，数据挖掘是在大型关系数据库几十个领域之间中寻找相互关系或模式的过程。
Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases.
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