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。
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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