The search space of Multi-relational data mining algorithm becomes larger and more complex.
多关系数据挖掘算法的搜索空间变得更大、更复杂。
That is why this paper chooses a multi-relational data mining algorithm as our research object.
因此本文以多关系数据挖掘算法作为研究对象。
For multi-relational data mining, how mining more efficiently and how improving the scalability of the algorithm, has been the focus of our study.
对于多关系的数据挖掘研究，如何高效地挖掘以及如何提高算法的可扩展性，一直是大家研究的重点。
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.
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。
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。
Traditional data mining algorithm is oriented relational database and data warehouse, and can not be directly used for data mining in XML documents.
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。
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。
Induction dependency relation is an important concept in relational databases research. Automatic discovering minimal induction dependency relation plays an important role in data mining.
归纳依赖关系是数据库研究领域的重要概念，在数据库中自动发现最小归纳依赖关系对数据采掘具有重大意义。
The classical data mining approaches can only look for patterns in single relation, and it is difficult to look for complex relational patterns which involved in multi-relational databases.
传统的数据挖掘方法只能从单一关系中进行模式发现，而很难在复杂的结构化数据中发现复杂的关系模式。
The classical data mining approaches can only look for patterns in single relation, and it is difficult to look for complex relational patterns which involved in multi-relational databases.
传统的数据挖掘方法只能从单一关系中进行模式发现，而很难在复杂的结构化数据中发现复杂的关系模式。
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