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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
应用推荐